Overview

Brought to you by YData

Dataset statistics

Number of variables37
Number of observations107003
Missing cells877667
Missing cells (%)22.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.2 MiB
Average record size in memory296.0 B

Variable types

Text11
Categorical20
DateTime1
Boolean1
Unsupported1
Numeric3

Alerts

Distance is highly overall correlated with Intersection Type and 1 other fieldsHigh correlation
Intersection Type is highly overall correlated with DistanceHigh correlation
Latitude is highly overall correlated with LongitudeHigh correlation
Longitude is highly overall correlated with LatitudeHigh correlation
Municipality is highly overall correlated with DistanceHigh correlation
Agency Name is highly imbalanced (56.9%) Imbalance
Distance Unit is highly imbalanced (76.3%) Imbalance
Road Grade is highly imbalanced (63.9%) Imbalance
Municipality is highly imbalanced (54.1%) Imbalance
Related Non-Motorist is highly imbalanced (63.4%) Imbalance
At Fault is highly imbalanced (72.2%) Imbalance
Weather is highly imbalanced (61.2%) Imbalance
Surface Condition is highly imbalanced (68.8%) Imbalance
Light is highly imbalanced (52.9%) Imbalance
Non-Motorist Substance Abuse is highly imbalanced (73.0%) Imbalance
Intersection Type is highly imbalanced (53.0%) Imbalance
Road Alignment is highly imbalanced (67.8%) Imbalance
Road Condition is highly imbalanced (87.6%) Imbalance
Road Division is highly imbalanced (57.2%) Imbalance
Hit/Run has 1765 (1.6%) missing values Missing
Route Type has 14143 (13.2%) missing values Missing
Lane Direction has 13499 (12.6%) missing values Missing
Lane Type has 89096 (83.3%) missing values Missing
Number of Lanes has 12340 (11.5%) missing values Missing
Direction has 13486 (12.6%) missing values Missing
Distance has 11623 (10.9%) missing values Missing
Distance Unit has 12328 (11.5%) missing values Missing
Road Grade has 14114 (13.2%) missing values Missing
Road Name has 14616 (13.7%) missing values Missing
Cross-Street Name has 18633 (17.4%) missing values Missing
Off-Road Description has 93529 (87.4%) missing values Missing
Municipality has 96512 (90.2%) missing values Missing
Related Non-Motorist has 101029 (94.4%) missing values Missing
Weather has 7948 (7.4%) missing values Missing
Surface Condition has 15843 (14.8%) missing values Missing
Traffic Control has 17550 (16.4%) missing values Missing
Driver Substance Abuse has 15672 (14.6%) missing values Missing
Non-Motorist Substance Abuse has 102147 (95.5%) missing values Missing
Second Harmful Event has 79023 (73.9%) missing values Missing
Junction has 27535 (25.7%) missing values Missing
Intersection Type has 55892 (52.2%) missing values Missing
Road Alignment has 13896 (13.0%) missing values Missing
Road Condition has 18803 (17.6%) missing values Missing
Road Division has 14676 (13.7%) missing values Missing
Report Number has unique values Unique
Number of Lanes is an unsupported type, check if it needs cleaning or further analysis Unsupported
Distance has 46669 (43.6%) zeros Zeros

Reproduction

Analysis started2024-12-04 18:01:08.672310
Analysis finished2024-12-04 18:02:01.769744
Duration53.1 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Report Number
Text

Unique 

Distinct107003
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size836.1 KiB
2024-12-04T13:02:02.031095image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.860667
Min length10

Characters and Unicode

Total characters1162124
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique107003 ?
Unique (%)100.0%

Sample

1st rowMCP1123002M
2nd rowMCP21610009
3rd rowMCP2790000P
4th rowMCP3378000J
5th rowDD5659000H
ValueCountFrequency (%)
mcp2688002t 1
 
< 0.1%
mcp2466005j 1
 
< 0.1%
mcp1123002m 1
 
< 0.1%
mcp21610009 1
 
< 0.1%
mcp2790000p 1
 
< 0.1%
mcp3378000j 1
 
< 0.1%
dd5659000h 1
 
< 0.1%
mcp33190021 1
 
< 0.1%
mcp3008003z 1
 
< 0.1%
mcp289200fc 1
 
< 0.1%
Other values (106993) 106993
> 99.9%
2024-12-04T13:02:02.537097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 273988
23.6%
2 103006
 
8.9%
M 97797
 
8.4%
C 96564
 
8.3%
P 95632
 
8.2%
3 78441
 
6.7%
1 72173
 
6.2%
5 46545
 
4.0%
8 42656
 
3.7%
6 41162
 
3.5%
Other values (23) 214160
18.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1162124
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 273988
23.6%
2 103006
 
8.9%
M 97797
 
8.4%
C 96564
 
8.3%
P 95632
 
8.2%
3 78441
 
6.7%
1 72173
 
6.2%
5 46545
 
4.0%
8 42656
 
3.7%
6 41162
 
3.5%
Other values (23) 214160
18.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1162124
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 273988
23.6%
2 103006
 
8.9%
M 97797
 
8.4%
C 96564
 
8.3%
P 95632
 
8.2%
3 78441
 
6.7%
1 72173
 
6.2%
5 46545
 
4.0%
8 42656
 
3.7%
6 41162
 
3.5%
Other values (23) 214160
18.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1162124
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 273988
23.6%
2 103006
 
8.9%
M 97797
 
8.4%
C 96564
 
8.3%
P 95632
 
8.2%
3 78441
 
6.7%
1 72173
 
6.2%
5 46545
 
4.0%
8 42656
 
3.7%
6 41162
 
3.5%
Other values (23) 214160
18.4%
Distinct106893
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size836.1 KiB
2024-12-04T13:02:02.920097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length11
Median length9
Mean length8.7409512
Min length4

Characters and Unicode

Total characters935308
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106783 ?
Unique (%)99.8%

Sample

1st row190010046
2nd row16028039
3rd row15041420
4th row230051006
5th row230049130
ValueCountFrequency (%)
230054902 2
 
< 0.1%
230002642 2
 
< 0.1%
15028525 2
 
< 0.1%
170529930 2
 
< 0.1%
220001121 2
 
< 0.1%
15000748 2
 
< 0.1%
16000781 2
 
< 0.1%
16001025 2
 
< 0.1%
230005622 2
 
< 0.1%
17001580 2
 
< 0.1%
Other values (106883) 106983
> 99.9%
2024-12-04T13:02:03.438096image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 248817
26.6%
1 129914
13.9%
2 118556
12.7%
5 76560
 
8.2%
3 69583
 
7.4%
4 69333
 
7.4%
6 60823
 
6.5%
7 54983
 
5.9%
9 53468
 
5.7%
8 53258
 
5.7%
Other values (2) 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 935308
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 248817
26.6%
1 129914
13.9%
2 118556
12.7%
5 76560
 
8.2%
3 69583
 
7.4%
4 69333
 
7.4%
6 60823
 
6.5%
7 54983
 
5.9%
9 53468
 
5.7%
8 53258
 
5.7%
Other values (2) 13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 935308
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 248817
26.6%
1 129914
13.9%
2 118556
12.7%
5 76560
 
8.2%
3 69583
 
7.4%
4 69333
 
7.4%
6 60823
 
6.5%
7 54983
 
5.9%
9 53468
 
5.7%
8 53258
 
5.7%
Other values (2) 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 935308
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 248817
26.6%
1 129914
13.9%
2 118556
12.7%
5 76560
 
8.2%
3 69583
 
7.4%
4 69333
 
7.4%
6 60823
 
6.5%
7 54983
 
5.9%
9 53468
 
5.7%
8 53258
 
5.7%
Other values (2) 13
 
< 0.1%

Agency Name
Categorical

Imbalance 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size836.1 KiB
Montgomery County Police
78093 
MONTGOMERY
14000 
Rockville Police Departme
 
5621
Gaithersburg Police Depar
 
4490
Takoma Park Police Depart
 
1783
Other values (5)
 
3016

Length

Max length25
Median length24
Mean length21.987869
Min length6

Characters and Unicode

Total characters2352768
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMontgomery County Police
2nd rowMontgomery County Police
3rd rowMONTGOMERY
4th rowMontgomery County Police
5th rowRockville Police Departme

Common Values

ValueCountFrequency (%)
Montgomery County Police 78093
73.0%
MONTGOMERY 14000
 
13.1%
Rockville Police Departme 5621
 
5.3%
Gaithersburg Police Depar 4490
 
4.2%
Takoma Park Police Depart 1783
 
1.7%
ROCKVILLE 1007
 
0.9%
GAITHERSBURG 821
 
0.8%
Maryland-National Capital 797
 
0.7%
TAKOMA 265
 
0.2%
MCPARK 126
 
0.1%

Length

2024-12-04T13:02:03.589095image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T13:02:03.727094image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
montgomery 92093
31.8%
police 89987
31.1%
county 78093
27.0%
rockville 6628
 
2.3%
departme 5621
 
1.9%
gaithersburg 5311
 
1.8%
depar 4490
 
1.6%
takoma 2048
 
0.7%
park 1783
 
0.6%
depart 1783
 
0.6%
Other values (3) 1720
 
0.6%

Most occurring characters

ValueCountFrequency (%)
o 332467
14.1%
e 195706
 
8.3%
182554
 
7.8%
t 169674
 
7.2%
n 157780
 
6.7%
y 156983
 
6.7%
M 107281
 
4.6%
l 103620
 
4.4%
i 101692
 
4.3%
r 101547
 
4.3%
Other values (32) 743464
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2352768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 332467
14.1%
e 195706
 
8.3%
182554
 
7.8%
t 169674
 
7.2%
n 157780
 
6.7%
y 156983
 
6.7%
M 107281
 
4.6%
l 103620
 
4.4%
i 101692
 
4.3%
r 101547
 
4.3%
Other values (32) 743464
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2352768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 332467
14.1%
e 195706
 
8.3%
182554
 
7.8%
t 169674
 
7.2%
n 157780
 
6.7%
y 156983
 
6.7%
M 107281
 
4.6%
l 103620
 
4.4%
i 101692
 
4.3%
r 101547
 
4.3%
Other values (32) 743464
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2352768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 332467
14.1%
e 195706
 
8.3%
182554
 
7.8%
t 169674
 
7.2%
n 157780
 
6.7%
y 156983
 
6.7%
M 107281
 
4.6%
l 103620
 
4.4%
i 101692
 
4.3%
r 101547
 
4.3%
Other values (32) 743464
31.6%

ACRS Report Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size836.1 KiB
Property Damage Crash
70300 
Injury Crash
36371 
Fatal Crash
 
332

Length

Max length21
Median length21
Mean length17.909816
Min length11

Characters and Unicode

Total characters1916404
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInjury Crash
2nd rowProperty Damage Crash
3rd rowProperty Damage Crash
4th rowInjury Crash
5th rowProperty Damage Crash

Common Values

ValueCountFrequency (%)
Property Damage Crash 70300
65.7%
Injury Crash 36371
34.0%
Fatal Crash 332
 
0.3%

Length

2024-12-04T13:02:03.891094image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T13:02:03.999095image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
crash 107003
37.6%
property 70300
24.7%
damage 70300
24.7%
injury 36371
 
12.8%
fatal 332
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 283974
14.8%
a 248267
13.0%
177303
 
9.3%
e 140600
 
7.3%
C 107003
 
5.6%
h 107003
 
5.6%
s 107003
 
5.6%
y 106671
 
5.6%
t 70632
 
3.7%
p 70300
 
3.7%
Other values (11) 497648
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1916404
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 283974
14.8%
a 248267
13.0%
177303
 
9.3%
e 140600
 
7.3%
C 107003
 
5.6%
h 107003
 
5.6%
s 107003
 
5.6%
y 106671
 
5.6%
t 70632
 
3.7%
p 70300
 
3.7%
Other values (11) 497648
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1916404
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 283974
14.8%
a 248267
13.0%
177303
 
9.3%
e 140600
 
7.3%
C 107003
 
5.6%
h 107003
 
5.6%
s 107003
 
5.6%
y 106671
 
5.6%
t 70632
 
3.7%
p 70300
 
3.7%
Other values (11) 497648
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1916404
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 283974
14.8%
a 248267
13.0%
177303
 
9.3%
e 140600
 
7.3%
C 107003
 
5.6%
h 107003
 
5.6%
s 107003
 
5.6%
y 106671
 
5.6%
t 70632
 
3.7%
p 70300
 
3.7%
Other values (11) 497648
26.0%
Distinct104371
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size836.1 KiB
Minimum2015-01-01 00:30:00
Maximum2024-11-27 02:40:00
2024-12-04T13:02:04.117105image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T13:02:04.250095image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Hit/Run
Boolean

Missing 

Distinct2
Distinct (%)< 0.1%
Missing1765
Missing (%)1.6%
Memory size209.1 KiB
False
87023 
True
18215 
(Missing)
 
1765
ValueCountFrequency (%)
False 87023
81.3%
True 18215
 
17.0%
(Missing) 1765
 
1.6%
2024-12-04T13:02:04.366095image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Route Type
Categorical

Missing 

Distinct19
Distinct (%)< 0.1%
Missing14143
Missing (%)13.2%
Memory size836.1 KiB
Maryland (State)
39121 
County
32679 
Municipality
5778 
US (State)
4092 
County Route
 
3425
Other values (14)
7765 

Length

Max length22
Median length20
Mean length11.97045
Min length4

Characters and Unicode

Total characters1111576
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaryland (State)
2nd rowCounty
3rd rowCounty
4th rowMaryland (State)
5th rowCounty

Common Values

ValueCountFrequency (%)
Maryland (State) 39121
36.6%
County 32679
30.5%
Municipality 5778
 
5.4%
US (State) 4092
 
3.8%
County Route 3425
 
3.2%
Maryland (State) Route 2947
 
2.8%
Interstate (State) 1758
 
1.6%
Other Public Roadway 788
 
0.7%
Municipality Route 732
 
0.7%
Ramp 518
 
0.5%
Other values (9) 1022
 
1.0%
(Missing) 14143
 
13.2%

Length

2024-12-04T13:02:04.480095image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
state 47918
32.0%
maryland 42068
28.1%
county 36104
24.1%
route 7543
 
5.0%
municipality 6510
 
4.3%
us 4092
 
2.7%
interstate 1758
 
1.2%
other 788
 
0.5%
public 788
 
0.5%
roadway 788
 
0.5%
Other values (10) 1566
 
1.0%

Most occurring characters

ValueCountFrequency (%)
t 152591
13.7%
a 142730
12.8%
n 87386
 
7.9%
y 85583
 
7.7%
e 60967
 
5.5%
57063
 
5.1%
S 52116
 
4.7%
u 51025
 
4.6%
l 49683
 
4.5%
M 48578
 
4.4%
Other values (24) 323854
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1111576
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 152591
13.7%
a 142730
12.8%
n 87386
 
7.9%
y 85583
 
7.7%
e 60967
 
5.5%
57063
 
5.1%
S 52116
 
4.7%
u 51025
 
4.6%
l 49683
 
4.5%
M 48578
 
4.4%
Other values (24) 323854
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1111576
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 152591
13.7%
a 142730
12.8%
n 87386
 
7.9%
y 85583
 
7.7%
e 60967
 
5.5%
57063
 
5.1%
S 52116
 
4.7%
u 51025
 
4.6%
l 49683
 
4.5%
M 48578
 
4.4%
Other values (24) 323854
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1111576
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 152591
13.7%
a 142730
12.8%
n 87386
 
7.9%
y 85583
 
7.7%
e 60967
 
5.5%
57063
 
5.1%
S 52116
 
4.7%
u 51025
 
4.6%
l 49683
 
4.5%
M 48578
 
4.4%
Other values (24) 323854
29.1%

Lane Direction
Categorical

Missing 

Distinct35
Distinct (%)< 0.1%
Missing13499
Missing (%)12.6%
Memory size836.1 KiB
North
25905 
South
23978 
East
17736 
West
16771 
Northbound
 
1901
Other values (30)
7213 

Length

Max length38
Median length5
Mean length5.3364669
Min length4

Characters and Unicode

Total characters498981
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowWest
2nd rowEast
3rd rowSouth
4th rowWest
5th rowSouth

Common Values

ValueCountFrequency (%)
North 25905
24.2%
South 23978
22.4%
East 17736
16.6%
West 16771
15.7%
Northbound 1901
 
1.8%
Southbound 1686
 
1.6%
Eastbound 1224
 
1.1%
Westbound 1175
 
1.1%
Unknown 871
 
0.8%
Northbound, Southbound 501
 
0.5%
Other values (25) 1756
 
1.6%
(Missing) 13499
12.6%

Length

2024-12-04T13:02:04.618095image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
north 25905
26.9%
south 23978
24.9%
east 17736
18.4%
west 16771
17.4%
northbound 3023
 
3.1%
southbound 2797
 
2.9%
eastbound 2233
 
2.3%
westbound 2155
 
2.2%
unknown 906
 
0.9%
not 219
 
0.2%
Other values (2) 438
 
0.5%

Most occurring characters

ValueCountFrequency (%)
t 94817
19.0%
o 67255
13.5%
h 55703
11.2%
s 38895
7.8%
u 36983
 
7.4%
N 29147
 
5.8%
r 28928
 
5.8%
S 26775
 
5.4%
a 20407
 
4.1%
E 19969
 
4.0%
Other values (13) 80102
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 498981
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 94817
19.0%
o 67255
13.5%
h 55703
11.2%
s 38895
7.8%
u 36983
 
7.4%
N 29147
 
5.8%
r 28928
 
5.8%
S 26775
 
5.4%
a 20407
 
4.1%
E 19969
 
4.0%
Other values (13) 80102
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 498981
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 94817
19.0%
o 67255
13.5%
h 55703
11.2%
s 38895
7.8%
u 36983
 
7.4%
N 29147
 
5.8%
r 28928
 
5.8%
S 26775
 
5.4%
a 20407
 
4.1%
E 19969
 
4.0%
Other values (13) 80102
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 498981
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 94817
19.0%
o 67255
13.5%
h 55703
11.2%
s 38895
7.8%
u 36983
 
7.4%
N 29147
 
5.8%
r 28928
 
5.8%
S 26775
 
5.4%
a 20407
 
4.1%
E 19969
 
4.0%
Other values (13) 80102
16.1%

Lane Type
Text

Missing 

Distinct151
Distinct (%)0.8%
Missing89096
Missing (%)83.3%
Memory size836.1 KiB
2024-12-04T13:02:04.764105image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length45
Median length43
Mean length10.772826
Min length5

Characters and Unicode

Total characters192909
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)0.3%

Sample

1st rowOTHER
2nd rowUNKNOWN
3rd rowOTHER
4th rowOTHER
5th rowLEFT TURN LANE
ValueCountFrequency (%)
lane 14339
32.4%
turn 5692
 
12.9%
1 5036
 
11.4%
left 3800
 
8.6%
2 2212
 
5.0%
right 2007
 
4.5%
area 1972
 
4.5%
other 1351
 
3.1%
shoulder 1114
 
2.5%
3 1069
 
2.4%
Other values (21) 5614
 
12.7%
2024-12-04T13:02:05.078093image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26299
13.6%
L 19122
 
9.9%
R 12852
 
6.7%
E 12793
 
6.6%
N 12399
 
6.4%
T 12013
 
6.2%
e 11429
 
5.9%
A 10863
 
5.6%
n 10419
 
5.4%
a 10258
 
5.3%
Other values (41) 54462
28.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 192909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
26299
13.6%
L 19122
 
9.9%
R 12852
 
6.7%
E 12793
 
6.6%
N 12399
 
6.4%
T 12013
 
6.2%
e 11429
 
5.9%
A 10863
 
5.6%
n 10419
 
5.4%
a 10258
 
5.3%
Other values (41) 54462
28.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 192909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
26299
13.6%
L 19122
 
9.9%
R 12852
 
6.7%
E 12793
 
6.6%
N 12399
 
6.4%
T 12013
 
6.2%
e 11429
 
5.9%
A 10863
 
5.6%
n 10419
 
5.4%
a 10258
 
5.3%
Other values (41) 54462
28.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 192909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
26299
13.6%
L 19122
 
9.9%
R 12852
 
6.7%
E 12793
 
6.6%
N 12399
 
6.4%
T 12013
 
6.2%
e 11429
 
5.9%
A 10863
 
5.6%
n 10419
 
5.4%
a 10258
 
5.3%
Other values (41) 54462
28.2%

Number of Lanes
Unsupported

Missing  Rejected  Unsupported 

Missing12340
Missing (%)11.5%
Memory size836.1 KiB

Direction
Categorical

Missing 

Distinct5
Distinct (%)< 0.1%
Missing13486
Missing (%)12.6%
Memory size836.1 KiB
North
38203 
East
22879 
South
19692 
West
12706 
Unknown
 
37

Length

Max length7
Median length5
Mean length4.6202722
Min length4

Characters and Unicode

Total characters432074
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEast
2nd rowEast
3rd rowSouth
4th rowWest
5th rowSouth

Common Values

ValueCountFrequency (%)
North 38203
35.7%
East 22879
21.4%
South 19692
18.4%
West 12706
 
11.9%
Unknown 37
 
< 0.1%
(Missing) 13486
 
12.6%

Length

2024-12-04T13:02:05.219095image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T13:02:05.341095image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
north 38203
40.9%
east 22879
24.5%
south 19692
21.1%
west 12706
 
13.6%
unknown 37
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
t 93480
21.6%
o 57932
13.4%
h 57895
13.4%
N 38203
8.8%
r 38203
8.8%
s 35585
 
8.2%
E 22879
 
5.3%
a 22879
 
5.3%
S 19692
 
4.6%
u 19692
 
4.6%
Other values (6) 25634
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 432074
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 93480
21.6%
o 57932
13.4%
h 57895
13.4%
N 38203
8.8%
r 38203
8.8%
s 35585
 
8.2%
E 22879
 
5.3%
a 22879
 
5.3%
S 19692
 
4.6%
u 19692
 
4.6%
Other values (6) 25634
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 432074
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 93480
21.6%
o 57932
13.4%
h 57895
13.4%
N 38203
8.8%
r 38203
8.8%
s 35585
 
8.2%
E 22879
 
5.3%
a 22879
 
5.3%
S 19692
 
4.6%
u 19692
 
4.6%
Other values (6) 25634
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 432074
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 93480
21.6%
o 57932
13.4%
h 57895
13.4%
N 38203
8.8%
r 38203
8.8%
s 35585
 
8.2%
E 22879
 
5.3%
a 22879
 
5.3%
S 19692
 
4.6%
u 19692
 
4.6%
Other values (6) 25634
 
5.9%

Distance
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct4129
Distinct (%)4.3%
Missing11623
Missing (%)10.9%
Infinite0
Infinite (%)0.0%
Mean58.312457
Minimum0
Maximum10078.32
Zeros46669
Zeros (%)43.6%
Negative0
Negative (%)0.0%
Memory size836.1 KiB
2024-12-04T13:02:05.477095image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.19
Q350
95-th percentile300
Maximum10078.32
Range10078.32
Interquartile range (IQR)50

Descriptive statistics

Standard deviation137.71824
Coefficient of variation (CV)2.3617293
Kurtosis353.26395
Mean58.312457
Median Absolute Deviation (MAD)0.19
Skewness8.5667619
Sum5561842.1
Variance18966.313
MonotonicityNot monotonic
2024-12-04T13:02:05.625095image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46669
43.6%
100 6397
 
6.0%
50 5487
 
5.1%
10 3217
 
3.0%
200 3096
 
2.9%
20 2797
 
2.6%
500 2350
 
2.2%
300 2104
 
2.0%
30 1693
 
1.6%
5 1615
 
1.5%
Other values (4119) 19955
18.6%
(Missing) 11623
 
10.9%
ValueCountFrequency (%)
0 46669
43.6%
0.01 66
 
0.1%
0.02 19
 
< 0.1%
0.03 7
 
< 0.1%
0.04 5
 
< 0.1%
0.05 50
 
< 0.1%
0.06 3
 
< 0.1%
0.07 2
 
< 0.1%
0.08 6
 
< 0.1%
0.09 3
 
< 0.1%
ValueCountFrequency (%)
10078.32 1
< 0.1%
5636.97 1
< 0.1%
3735.52 1
< 0.1%
3486.01 1
< 0.1%
3416.01 1
< 0.1%
3126.95 1
< 0.1%
3091.93 1
< 0.1%
2403.22 1
< 0.1%
2038.97 1
< 0.1%
1919.86 1
< 0.1%

Distance Unit
Categorical

Imbalance  Missing 

Distinct3
Distinct (%)< 0.1%
Missing12328
Missing (%)11.5%
Memory size836.1 KiB
FEET
88424 
MILE
 
5792
UNKNOWN
 
459

Length

Max length7
Median length4
Mean length4.0145445
Min length4

Characters and Unicode

Total characters380077
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFEET
2nd rowFEET
3rd rowFEET
4th rowFEET
5th rowFEET

Common Values

ValueCountFrequency (%)
FEET 88424
82.6%
MILE 5792
 
5.4%
UNKNOWN 459
 
0.4%
(Missing) 12328
 
11.5%

Length

2024-12-04T13:02:05.772107image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T13:02:05.873093image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
feet 88424
93.4%
mile 5792
 
6.1%
unknown 459
 
0.5%

Most occurring characters

ValueCountFrequency (%)
E 182640
48.1%
F 88424
23.3%
T 88424
23.3%
M 5792
 
1.5%
I 5792
 
1.5%
L 5792
 
1.5%
N 1377
 
0.4%
U 459
 
0.1%
K 459
 
0.1%
O 459
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 380077
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 182640
48.1%
F 88424
23.3%
T 88424
23.3%
M 5792
 
1.5%
I 5792
 
1.5%
L 5792
 
1.5%
N 1377
 
0.4%
U 459
 
0.1%
K 459
 
0.1%
O 459
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 380077
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 182640
48.1%
F 88424
23.3%
T 88424
23.3%
M 5792
 
1.5%
I 5792
 
1.5%
L 5792
 
1.5%
N 1377
 
0.4%
U 459
 
0.1%
K 459
 
0.1%
O 459
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 380077
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 182640
48.1%
F 88424
23.3%
T 88424
23.3%
M 5792
 
1.5%
I 5792
 
1.5%
L 5792
 
1.5%
N 1377
 
0.4%
U 459
 
0.1%
K 459
 
0.1%
O 459
 
0.1%

Road Grade
Categorical

Imbalance  Missing 

Distinct22
Distinct (%)< 0.1%
Missing14114
Missing (%)13.2%
Memory size836.1 KiB
LEVEL
64578 
GRADE DOWNHILL
9866 
HILL UPHILL
7323 
Level
6558 
HILL CREST
 
2185
Other values (17)
 
2379

Length

Max length24
Median length5
Mean length6.6378689
Min length5

Characters and Unicode

Total characters616585
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowGRADE DOWNHILL
2nd rowLEVEL
3rd rowLEVEL
4th rowLEVEL
5th rowLEVEL

Common Values

ValueCountFrequency (%)
LEVEL 64578
60.4%
GRADE DOWNHILL 9866
 
9.2%
HILL UPHILL 7323
 
6.8%
Level 6558
 
6.1%
HILL CREST 2185
 
2.0%
Uphill 635
 
0.6%
Downhill 635
 
0.6%
OTHER 190
 
0.2%
Downhill, Uphill 168
 
0.2%
UNKNOWN 152
 
0.1%
Other values (12) 599
 
0.6%
(Missing) 14114
 
13.2%

Length

2024-12-04T13:02:06.006109image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
level 71396
63.2%
downhill 10798
 
9.6%
grade 9866
 
8.7%
hill 9508
 
8.4%
uphill 8265
 
7.3%
crest 2185
 
1.9%
other 190
 
0.2%
unknown 152
 
0.1%
hillcrest 136
 
0.1%
dip 131
 
0.1%
Other values (4) 300
 
0.3%

Most occurring characters

ValueCountFrequency (%)
L 189368
30.7%
E 141471
22.9%
V 64578
 
10.5%
H 27023
 
4.4%
I 26902
 
4.4%
D 20869
 
3.4%
20038
 
3.2%
e 13772
 
2.2%
R 12315
 
2.0%
l 10838
 
1.8%
Other values (30) 89411
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 616585
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 189368
30.7%
E 141471
22.9%
V 64578
 
10.5%
H 27023
 
4.4%
I 26902
 
4.4%
D 20869
 
3.4%
20038
 
3.2%
e 13772
 
2.2%
R 12315
 
2.0%
l 10838
 
1.8%
Other values (30) 89411
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 616585
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 189368
30.7%
E 141471
22.9%
V 64578
 
10.5%
H 27023
 
4.4%
I 26902
 
4.4%
D 20869
 
3.4%
20038
 
3.2%
e 13772
 
2.2%
R 12315
 
2.0%
l 10838
 
1.8%
Other values (30) 89411
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 616585
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 189368
30.7%
E 141471
22.9%
V 64578
 
10.5%
H 27023
 
4.4%
I 26902
 
4.4%
D 20869
 
3.4%
20038
 
3.2%
e 13772
 
2.2%
R 12315
 
2.0%
l 10838
 
1.8%
Other values (30) 89411
14.5%

Road Name
Text

Missing 

Distinct4409
Distinct (%)4.8%
Missing14616
Missing (%)13.7%
Memory size836.1 KiB
2024-12-04T13:02:06.329097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length43
Median length38
Mean length13.299512
Min length4

Characters and Unicode

Total characters1228702
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1986 ?
Unique (%)2.1%

Sample

1st rowNORBECK RD
2nd rowTHORNAPPLE ST
3rd rowVALLEY BEND DR
4th rowUNIVERSITY BLVD W
5th rowROCKVILLE PIKE
ValueCountFrequency (%)
rd 39905
 
17.8%
ave 20891
 
9.3%
dr 7602
 
3.4%
georgia 5769
 
2.6%
blvd 5305
 
2.4%
pike 5097
 
2.3%
mill 3848
 
1.7%
new 3686
 
1.6%
hampshire 3682
 
1.6%
st 3430
 
1.5%
Other values (3304) 125531
55.9%
2024-12-04T13:02:06.836097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
132377
 
10.8%
R 126063
 
10.3%
E 125858
 
10.2%
D 84718
 
6.9%
A 81223
 
6.6%
I 69568
 
5.7%
L 69444
 
5.7%
O 65417
 
5.3%
N 56944
 
4.6%
S 46117
 
3.8%
Other values (34) 370973
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1228702
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
132377
 
10.8%
R 126063
 
10.3%
E 125858
 
10.2%
D 84718
 
6.9%
A 81223
 
6.6%
I 69568
 
5.7%
L 69444
 
5.7%
O 65417
 
5.3%
N 56944
 
4.6%
S 46117
 
3.8%
Other values (34) 370973
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1228702
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
132377
 
10.8%
R 126063
 
10.3%
E 125858
 
10.2%
D 84718
 
6.9%
A 81223
 
6.6%
I 69568
 
5.7%
L 69444
 
5.7%
O 65417
 
5.3%
N 56944
 
4.6%
S 46117
 
3.8%
Other values (34) 370973
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1228702
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
132377
 
10.8%
R 126063
 
10.3%
E 125858
 
10.2%
D 84718
 
6.9%
A 81223
 
6.6%
I 69568
 
5.7%
L 69444
 
5.7%
O 65417
 
5.3%
N 56944
 
4.6%
S 46117
 
3.8%
Other values (34) 370973
30.2%

Cross-Street Name
Text

Missing 

Distinct7270
Distinct (%)8.2%
Missing18633
Missing (%)17.4%
Memory size836.1 KiB
2024-12-04T13:02:07.135097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length81
Median length63
Mean length13.641372
Min length3

Characters and Unicode

Total characters1205488
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2692 ?
Unique (%)3.0%

Sample

1st rowWINTERGATE DR
2nd rowLENHART DR
3rd rowCROSS LAUREL DR
4th rowELKIN ST
5th rowPARK RD
ValueCountFrequency (%)
rd 28637
 
12.0%
dr 15267
 
6.4%
ave 13545
 
5.7%
st 5927
 
2.5%
to 5821
 
2.4%
la 4861
 
2.0%
blvd 3802
 
1.6%
fr 3783
 
1.6%
ramp 3609
 
1.5%
md 2578
 
1.1%
Other values (4662) 150774
63.2%
2024-12-04T13:02:07.610097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
150346
12.5%
R 121681
 
10.1%
E 106353
 
8.8%
D 82360
 
6.8%
A 81092
 
6.7%
O 64735
 
5.4%
L 64216
 
5.3%
N 59165
 
4.9%
T 53633
 
4.4%
S 53100
 
4.4%
Other values (38) 368807
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1205488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
150346
12.5%
R 121681
 
10.1%
E 106353
 
8.8%
D 82360
 
6.8%
A 81092
 
6.7%
O 64735
 
5.4%
L 64216
 
5.3%
N 59165
 
4.9%
T 53633
 
4.4%
S 53100
 
4.4%
Other values (38) 368807
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1205488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
150346
12.5%
R 121681
 
10.1%
E 106353
 
8.8%
D 82360
 
6.8%
A 81092
 
6.7%
O 64735
 
5.4%
L 64216
 
5.3%
N 59165
 
4.9%
T 53633
 
4.4%
S 53100
 
4.4%
Other values (38) 368807
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1205488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
150346
12.5%
R 121681
 
10.1%
E 106353
 
8.8%
D 82360
 
6.8%
A 81092
 
6.7%
O 64735
 
5.4%
L 64216
 
5.3%
N 59165
 
4.9%
T 53633
 
4.4%
S 53100
 
4.4%
Other values (38) 368807
30.6%

Off-Road Description
Text

Missing 

Distinct12673
Distinct (%)94.1%
Missing93529
Missing (%)87.4%
Memory size836.1 KiB
2024-12-04T13:02:08.029098image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length187
Median length111
Mean length46.750853
Min length4

Characters and Unicode

Total characters629921
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12257 ?
Unique (%)91.0%

Sample

1st rowParking Lot of 10401 Fernwood Rd
2nd rowPARKING LOT
3rd row45 W WATKINS MILL RD.
4th rowIn the parking lot in front of 14425 Parkvale Road.
5th rowGRASS DITCH IN FRONT OF 14 CHESTNUT STREET
ValueCountFrequency (%)
parking 11407
 
10.6%
lot 10389
 
9.6%
of 8421
 
7.8%
md 3143
 
2.9%
rd 2561
 
2.4%
ave 2204
 
2.0%
road 1437
 
1.3%
at 1335
 
1.2%
in 1279
 
1.2%
spring 1243
 
1.2%
Other values (7835) 64329
59.7%
2024-12-04T13:02:08.603120image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
103216
16.4%
R 38994
 
6.2%
O 35819
 
5.7%
A 34523
 
5.5%
E 32507
 
5.2%
I 28712
 
4.6%
N 28385
 
4.5%
L 26445
 
4.2%
T 26002
 
4.1%
G 19456
 
3.1%
Other values (69) 255862
40.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 629921
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
103216
16.4%
R 38994
 
6.2%
O 35819
 
5.7%
A 34523
 
5.5%
E 32507
 
5.2%
I 28712
 
4.6%
N 28385
 
4.5%
L 26445
 
4.2%
T 26002
 
4.1%
G 19456
 
3.1%
Other values (69) 255862
40.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 629921
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
103216
16.4%
R 38994
 
6.2%
O 35819
 
5.7%
A 34523
 
5.5%
E 32507
 
5.2%
I 28712
 
4.6%
N 28385
 
4.5%
L 26445
 
4.2%
T 26002
 
4.1%
G 19456
 
3.1%
Other values (69) 255862
40.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 629921
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
103216
16.4%
R 38994
 
6.2%
O 35819
 
5.7%
A 34523
 
5.5%
E 32507
 
5.2%
I 28712
 
4.6%
N 28385
 
4.5%
L 26445
 
4.2%
T 26002
 
4.1%
G 19456
 
3.1%
Other values (69) 255862
40.6%

Municipality
Categorical

High correlation  Imbalance  Missing 

Distinct20
Distinct (%)0.2%
Missing96512
Missing (%)90.2%
Memory size836.1 KiB
ROCKVILLE
5074 
GAITHERSBURG
3480 
TAKOMA PARK
989 
KENSINGTON
 
221
CHEVY CHASE #4
 
174
Other values (15)
553 

Length

Max length19
Median length18
Mean length10.560004
Min length8

Characters and Unicode

Total characters110785
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowROCKVILLE
2nd rowROCKVILLE
3rd rowROCKVILLE
4th rowGAITHERSBURG
5th rowROCKVILLE

Common Values

ValueCountFrequency (%)
ROCKVILLE 5074
 
4.7%
GAITHERSBURG 3480
 
3.3%
TAKOMA PARK 989
 
0.9%
KENSINGTON 221
 
0.2%
CHEVY CHASE #4 174
 
0.2%
CHEVY CHASE #3 83
 
0.1%
FRIENDSHIP HEIGHTS 80
 
0.1%
POOLESVILLE 77
 
0.1%
CHEVY CHASE VIEW 48
 
< 0.1%
CHEVY CHASE VILLAGE 46
 
< 0.1%
Other values (10) 219
 
0.2%
(Missing) 96512
90.2%

Length

2024-12-04T13:02:08.931106image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rockville 5074
40.7%
gaithersburg 3480
27.9%
park 1021
 
8.2%
takoma 989
 
7.9%
chevy 406
 
3.3%
chase 406
 
3.3%
kensington 221
 
1.8%
4 174
 
1.4%
3 83
 
0.7%
friendship 80
 
0.6%
Other values (17) 535
 
4.3%

Most occurring characters

ValueCountFrequency (%)
R 13295
12.0%
L 10563
 
9.5%
E 10197
 
9.2%
I 9304
 
8.4%
G 7409
 
6.7%
K 7318
 
6.6%
A 7047
 
6.4%
O 6628
 
6.0%
C 5904
 
5.3%
V 5706
 
5.2%
Other values (17) 27414
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 110785
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 13295
12.0%
L 10563
 
9.5%
E 10197
 
9.2%
I 9304
 
8.4%
G 7409
 
6.7%
K 7318
 
6.6%
A 7047
 
6.4%
O 6628
 
6.0%
C 5904
 
5.3%
V 5706
 
5.2%
Other values (17) 27414
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 110785
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 13295
12.0%
L 10563
 
9.5%
E 10197
 
9.2%
I 9304
 
8.4%
G 7409
 
6.7%
K 7318
 
6.6%
A 7047
 
6.4%
O 6628
 
6.0%
C 5904
 
5.3%
V 5706
 
5.2%
Other values (17) 27414
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 110785
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 13295
12.0%
L 10563
 
9.5%
E 10197
 
9.2%
I 9304
 
8.4%
G 7409
 
6.7%
K 7318
 
6.6%
A 7047
 
6.4%
O 6628
 
6.0%
C 5904
 
5.3%
V 5706
 
5.2%
Other values (17) 27414
24.7%

Related Non-Motorist
Categorical

Imbalance  Missing 

Distinct28
Distinct (%)0.5%
Missing101029
Missing (%)94.4%
Memory size836.1 KiB
PEDESTRIAN
3813 
BICYCLIST
1161 
Pedestrian
399 
OTHER
 
240
Cyclist (non-electric)
 
104
Other values (23)
 
257

Length

Max length86
Median length10
Mean length10.337128
Min length5

Characters and Unicode

Total characters61754
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st rowPEDESTRIAN
2nd rowPEDESTRIAN
3rd rowPEDESTRIAN
4th rowPEDESTRIAN
5th rowOTHER CONVEYANCE

Common Values

ValueCountFrequency (%)
PEDESTRIAN 3813
 
3.6%
BICYCLIST 1161
 
1.1%
Pedestrian 399
 
0.4%
OTHER 240
 
0.2%
Cyclist (non-electric) 104
 
0.1%
OTHER CONVEYANCE 80
 
0.1%
MACHINE OPERATOR/RIDER 38
 
< 0.1%
Scooter (electric) 32
 
< 0.1%
OTHER PEDALCYCLIST 26
 
< 0.1%
Cyclist (Electric) 19
 
< 0.1%
Other values (18) 62
 
0.1%
(Missing) 101029
94.4%

Length

2024-12-04T13:02:09.072093image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pedestrian 4243
65.3%
bicyclist 1170
 
18.0%
other 377
 
5.8%
cyclist 123
 
1.9%
non-electric 109
 
1.7%
conveyance 96
 
1.5%
electric 57
 
0.9%
operator/rider 39
 
0.6%
machine 39
 
0.6%
scooter 36
 
0.6%
Other values (23) 206
 
3.2%

Most occurring characters

ValueCountFrequency (%)
E 8355
13.5%
I 6275
10.2%
T 5441
8.8%
S 5061
 
8.2%
R 4350
 
7.0%
P 4308
 
7.0%
N 4050
 
6.6%
A 4019
 
6.5%
D 3897
 
6.3%
C 2720
 
4.4%
Other values (37) 13278
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 8355
13.5%
I 6275
10.2%
T 5441
8.8%
S 5061
 
8.2%
R 4350
 
7.0%
P 4308
 
7.0%
N 4050
 
6.6%
A 4019
 
6.5%
D 3897
 
6.3%
C 2720
 
4.4%
Other values (37) 13278
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 8355
13.5%
I 6275
10.2%
T 5441
8.8%
S 5061
 
8.2%
R 4350
 
7.0%
P 4308
 
7.0%
N 4050
 
6.6%
A 4019
 
6.5%
D 3897
 
6.3%
C 2720
 
4.4%
Other values (37) 13278
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 8355
13.5%
I 6275
10.2%
T 5441
8.8%
S 5061
 
8.2%
R 4350
 
7.0%
P 4308
 
7.0%
N 4050
 
6.6%
A 4019
 
6.5%
D 3897
 
6.3%
C 2720
 
4.4%
Other values (37) 13278
21.5%

At Fault
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size836.1 KiB
DRIVER
95745 
UNKNOWN
9655 
NONMOTORIST
 
1409
BOTH
 
194

Length

Max length11
Median length6
Mean length6.1524443
Min length4

Characters and Unicode

Total characters658330
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDRIVER
2nd rowDRIVER
3rd rowUNKNOWN
4th rowDRIVER
5th rowDRIVER

Common Values

ValueCountFrequency (%)
DRIVER 95745
89.5%
UNKNOWN 9655
 
9.0%
NONMOTORIST 1409
 
1.3%
BOTH 194
 
0.2%

Length

2024-12-04T13:02:09.213095image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-04T13:02:09.321097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
driver 95745
89.5%
unknown 9655
 
9.0%
nonmotorist 1409
 
1.3%
both 194
 
0.2%

Most occurring characters

ValueCountFrequency (%)
R 192899
29.3%
I 97154
14.8%
D 95745
14.5%
V 95745
14.5%
E 95745
14.5%
N 31783
 
4.8%
O 14076
 
2.1%
U 9655
 
1.5%
K 9655
 
1.5%
W 9655
 
1.5%
Other values (5) 6218
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 658330
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 192899
29.3%
I 97154
14.8%
D 95745
14.5%
V 95745
14.5%
E 95745
14.5%
N 31783
 
4.8%
O 14076
 
2.1%
U 9655
 
1.5%
K 9655
 
1.5%
W 9655
 
1.5%
Other values (5) 6218
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 658330
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 192899
29.3%
I 97154
14.8%
D 95745
14.5%
V 95745
14.5%
E 95745
14.5%
N 31783
 
4.8%
O 14076
 
2.1%
U 9655
 
1.5%
K 9655
 
1.5%
W 9655
 
1.5%
Other values (5) 6218
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 658330
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 192899
29.3%
I 97154
14.8%
D 95745
14.5%
V 95745
14.5%
E 95745
14.5%
N 31783
 
4.8%
O 14076
 
2.1%
U 9655
 
1.5%
K 9655
 
1.5%
W 9655
 
1.5%
Other values (5) 6218
 
0.9%

Collision Type
Categorical

Distinct28
Distinct (%)< 0.1%
Missing504
Missing (%)0.5%
Memory size836.1 KiB
SAME DIR REAR END
25739 
SINGLE VEHICLE
16096 
STRAIGHT MOVEMENT ANGLE
15240 
OTHER
13263 
SAME DIRECTION SIDESWIPE
8891 
Other values (23)
27270 

Length

Max length29
Median length27
Mean length16.544343
Min length5

Characters and Unicode

Total characters1761956
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSAME DIR REAR END
2nd rowOTHER
3rd rowOPPOSITE DIRECTION SIDESWIPE
4th rowSINGLE VEHICLE
5th rowSAME DIRECTION SIDESWIPE

Common Values

ValueCountFrequency (%)
SAME DIR REAR END 25739
24.1%
SINGLE VEHICLE 16096
15.0%
STRAIGHT MOVEMENT ANGLE 15240
14.2%
OTHER 13263
12.4%
SAME DIRECTION SIDESWIPE 8891
 
8.3%
HEAD ON LEFT TURN 6334
 
5.9%
Front to Rear 2431
 
2.3%
HEAD ON 2068
 
1.9%
SAME DIRECTION RIGHT TURN 1985
 
1.9%
Single Vehicle 1919
 
1.8%
Other values (18) 12533
11.7%

Length

2024-12-04T13:02:09.451098image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
same 40991
13.3%
rear 28907
 
9.4%
dir 26990
 
8.8%
end 25739
 
8.4%
angle 18739
 
6.1%
single 18015
 
5.9%
vehicle 18015
 
5.9%
direction 16118
 
5.2%
straight 15240
 
5.0%
movement 15240
 
5.0%
Other values (15) 83601
27.2%

Most occurring characters

ValueCountFrequency (%)
E 261045
14.8%
201096
11.4%
R 141141
 
8.0%
I 128740
 
7.3%
N 113207
 
6.4%
A 108006
 
6.1%
T 104472
 
5.9%
S 101208
 
5.7%
D 88818
 
5.0%
M 71963
 
4.1%
Other values (30) 442260
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1761956
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 261045
14.8%
201096
11.4%
R 141141
 
8.0%
I 128740
 
7.3%
N 113207
 
6.4%
A 108006
 
6.1%
T 104472
 
5.9%
S 101208
 
5.7%
D 88818
 
5.0%
M 71963
 
4.1%
Other values (30) 442260
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1761956
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 261045
14.8%
201096
11.4%
R 141141
 
8.0%
I 128740
 
7.3%
N 113207
 
6.4%
A 108006
 
6.1%
T 104472
 
5.9%
S 101208
 
5.7%
D 88818
 
5.0%
M 71963
 
4.1%
Other values (30) 442260
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1761956
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 261045
14.8%
201096
11.4%
R 141141
 
8.0%
I 128740
 
7.3%
N 113207
 
6.4%
A 108006
 
6.1%
T 104472
 
5.9%
S 101208
 
5.7%
D 88818
 
5.0%
M 71963
 
4.1%
Other values (30) 442260
25.1%

Weather
Categorical

Imbalance  Missing 

Distinct22
Distinct (%)< 0.1%
Missing7948
Missing (%)7.4%
Memory size836.1 KiB
CLEAR
65517 
RAINING
11674 
CLOUDY
9609 
Clear
7439 
Rain
 
1076
Other values (17)
 
3740

Length

Max length33
Median length5
Mean length5.3672606
Min length4

Characters and Unicode

Total characters531654
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCLOUDY
2nd rowCLEAR
3rd rowCLEAR
4th rowCLOUDY
5th rowCLEAR

Common Values

ValueCountFrequency (%)
CLEAR 65517
61.2%
RAINING 11674
 
10.9%
CLOUDY 9609
 
9.0%
Clear 7439
 
7.0%
Rain 1076
 
1.0%
SNOW 877
 
0.8%
Cloudy 794
 
0.7%
UNKNOWN 649
 
0.6%
FOGGY 429
 
0.4%
WINTRY MIX 252
 
0.2%
Other values (12) 739
 
0.7%
(Missing) 7948
 
7.4%

Length

2024-12-04T13:02:09.578104image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
clear 72956
73.3%
raining 11674
 
11.7%
cloudy 10403
 
10.4%
rain 1087
 
1.1%
snow 1045
 
1.0%
unknown 745
 
0.7%
foggy 429
 
0.4%
wintry 252
 
0.3%
mix 252
 
0.3%
other 214
 
0.2%
Other values (15) 529
 
0.5%

Most occurring characters

ValueCountFrequency (%)
C 83364
15.7%
R 78844
14.8%
A 77198
14.5%
L 75338
14.2%
E 66270
12.5%
N 26667
 
5.0%
I 24034
 
4.5%
G 12607
 
2.4%
O 11941
 
2.2%
U 10354
 
1.9%
Other values (32) 65037
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 531654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 83364
15.7%
R 78844
14.8%
A 77198
14.5%
L 75338
14.2%
E 66270
12.5%
N 26667
 
5.0%
I 24034
 
4.5%
G 12607
 
2.4%
O 11941
 
2.2%
U 10354
 
1.9%
Other values (32) 65037
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 531654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 83364
15.7%
R 78844
14.8%
A 77198
14.5%
L 75338
14.2%
E 66270
12.5%
N 26667
 
5.0%
I 24034
 
4.5%
G 12607
 
2.4%
O 11941
 
2.2%
U 10354
 
1.9%
Other values (32) 65037
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 531654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 83364
15.7%
R 78844
14.8%
A 77198
14.5%
L 75338
14.2%
E 66270
12.5%
N 26667
 
5.0%
I 24034
 
4.5%
G 12607
 
2.4%
O 11941
 
2.2%
U 10354
 
1.9%
Other values (32) 65037
12.2%

Surface Condition
Categorical

Imbalance  Missing 

Distinct20
Distinct (%)< 0.1%
Missing15843
Missing (%)14.8%
Memory size836.1 KiB
DRY
64948 
WET
15859 
Dry
6861 
Wet
 
1343
ICE
 
682
Other values (15)
 
1467

Length

Max length24
Median length3
Mean length3.0471259
Min length3

Characters and Unicode

Total characters277776
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDRY
2nd rowDRY
3rd rowDRY
4th rowDRY
5th rowDRY

Common Values

ValueCountFrequency (%)
DRY 64948
60.7%
WET 15859
 
14.8%
Dry 6861
 
6.4%
Wet 1343
 
1.3%
ICE 682
 
0.6%
SNOW 602
 
0.6%
UNKNOWN 407
 
0.4%
SLUSH 132
 
0.1%
OTHER 99
 
0.1%
Snow 54
 
0.1%
Other values (10) 173
 
0.2%
(Missing) 15843
 
14.8%

Length

2024-12-04T13:02:09.707096image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dry 71809
78.7%
wet 17202
 
18.9%
ice 682
 
0.7%
snow 656
 
0.7%
unknown 407
 
0.4%
slush 149
 
0.2%
other 115
 
0.1%
ice/frost 43
 
< 0.1%
mud 39
 
< 0.1%
dirt 39
 
< 0.1%
Other values (7) 107
 
0.1%

Most occurring characters

ValueCountFrequency (%)
D 71917
25.9%
R 65149
23.5%
Y 64948
23.4%
W 18246
 
6.6%
E 16706
 
6.0%
T 16054
 
5.8%
r 6931
 
2.5%
y 6861
 
2.5%
N 1916
 
0.7%
t 1415
 
0.5%
Other values (33) 7633
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 277776
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 71917
25.9%
R 65149
23.5%
Y 64948
23.4%
W 18246
 
6.6%
E 16706
 
6.0%
T 16054
 
5.8%
r 6931
 
2.5%
y 6861
 
2.5%
N 1916
 
0.7%
t 1415
 
0.5%
Other values (33) 7633
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 277776
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 71917
25.9%
R 65149
23.5%
Y 64948
23.4%
W 18246
 
6.6%
E 16706
 
6.0%
T 16054
 
5.8%
r 6931
 
2.5%
y 6861
 
2.5%
N 1916
 
0.7%
t 1415
 
0.5%
Other values (33) 7633
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 277776
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 71917
25.9%
R 65149
23.5%
Y 64948
23.4%
W 18246
 
6.6%
E 16706
 
6.0%
T 16054
 
5.8%
r 6931
 
2.5%
y 6861
 
2.5%
N 1916
 
0.7%
t 1415
 
0.5%
Other values (33) 7633
 
2.7%

Light
Categorical

Imbalance 

Distinct16
Distinct (%)< 0.1%
Missing815
Missing (%)0.8%
Memory size836.1 KiB
DAYLIGHT
63190 
DARK LIGHTS ON
23739 
Daylight
6489 
DARK NO LIGHTS
 
3504
Dark - Lighted
 
2201
Other values (11)
7065 

Length

Max length24
Median length8
Mean length9.7005406
Min length4

Characters and Unicode

Total characters1030081
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDAYLIGHT
2nd rowDAYLIGHT
3rd rowDARK LIGHTS ON
4th rowDAYLIGHT
5th rowDAYLIGHT

Common Values

ValueCountFrequency (%)
DAYLIGHT 63190
59.1%
DARK LIGHTS ON 23739
 
22.2%
Daylight 6489
 
6.1%
DARK NO LIGHTS 3504
 
3.3%
Dark - Lighted 2201
 
2.1%
DUSK 2182
 
2.0%
DAWN 2004
 
1.9%
DARK -- UNKNOWN LIGHTING 1113
 
1.0%
UNKNOWN 699
 
0.7%
Dark - Not Lighted 424
 
0.4%
Other values (6) 643
 
0.6%
(Missing) 815
 
0.8%

Length

2024-12-04T13:02:09.845095image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
daylight 69679
41.0%
dark 31057
18.3%
lights 27243
 
16.0%
on 23739
 
14.0%
3814
 
2.2%
no 3504
 
2.1%
lighted 2625
 
1.5%
dusk 2316
 
1.4%
dawn 2117
 
1.2%
unknown 1946
 
1.1%
Other values (3) 1875
 
1.1%

Most occurring characters

ValueCountFrequency (%)
D 105169
10.2%
L 94247
9.1%
A 93550
9.1%
I 92659
9.0%
G 92659
9.0%
H 91767
8.9%
T 91767
8.9%
63727
 
6.2%
Y 63190
 
6.1%
N 36220
 
3.5%
Other values (24) 205126
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1030081
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 105169
10.2%
L 94247
9.1%
A 93550
9.1%
I 92659
9.0%
G 92659
9.0%
H 91767
8.9%
T 91767
8.9%
63727
 
6.2%
Y 63190
 
6.1%
N 36220
 
3.5%
Other values (24) 205126
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1030081
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 105169
10.2%
L 94247
9.1%
A 93550
9.1%
I 92659
9.0%
G 92659
9.0%
H 91767
8.9%
T 91767
8.9%
63727
 
6.2%
Y 63190
 
6.1%
N 36220
 
3.5%
Other values (24) 205126
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1030081
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 105169
10.2%
L 94247
9.1%
A 93550
9.1%
I 92659
9.0%
G 92659
9.0%
H 91767
8.9%
T 91767
8.9%
63727
 
6.2%
Y 63190
 
6.1%
N 36220
 
3.5%
Other values (24) 205126
19.9%

Traffic Control
Text

Missing 

Distinct66
Distinct (%)0.1%
Missing17550
Missing (%)16.4%
Memory size836.1 KiB
2024-12-04T13:02:10.022096image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length96
Median length11
Mean length12.387388
Min length5

Characters and Unicode

Total characters1108089
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)< 0.1%

Sample

1st rowNO CONTROLS
2nd rowNO CONTROLS
3rd rowNO CONTROLS
4th rowNO CONTROLS
5th rowNO CONTROLS
ValueCountFrequency (%)
no 46508
25.3%
controls 46508
25.3%
signal 33201
18.1%
traffic 33096
18.0%
sign 8650
 
4.7%
stop 7414
 
4.0%
control 3021
 
1.6%
flashing 1340
 
0.7%
other 1257
 
0.7%
yield 1003
 
0.5%
Other values (36) 1477
 
0.8%
2024-12-04T13:02:10.328097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 133198
12.0%
N 128248
11.6%
94022
8.5%
S 92251
8.3%
T 82502
 
7.4%
C 79894
 
7.2%
L 73937
 
6.7%
R 73240
 
6.6%
I 70280
 
6.3%
F 61650
 
5.6%
Other values (40) 218867
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1108089
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 133198
12.0%
N 128248
11.6%
94022
8.5%
S 92251
8.3%
T 82502
 
7.4%
C 79894
 
7.2%
L 73937
 
6.7%
R 73240
 
6.6%
I 70280
 
6.3%
F 61650
 
5.6%
Other values (40) 218867
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1108089
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 133198
12.0%
N 128248
11.6%
94022
8.5%
S 92251
8.3%
T 82502
 
7.4%
C 79894
 
7.2%
L 73937
 
6.7%
R 73240
 
6.6%
I 70280
 
6.3%
F 61650
 
5.6%
Other values (40) 218867
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1108089
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 133198
12.0%
N 128248
11.6%
94022
8.5%
S 92251
8.3%
T 82502
 
7.4%
C 79894
 
7.2%
L 73937
 
6.7%
R 73240
 
6.6%
I 70280
 
6.3%
F 61650
 
5.6%
Other values (40) 218867
19.8%
Distinct103
Distinct (%)0.1%
Missing15672
Missing (%)14.6%
Memory size836.1 KiB
2024-12-04T13:02:10.486096image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length405
Median length13
Mean length21.425671
Min length5

Characters and Unicode

Total characters1956828
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)< 0.1%

Sample

1st rowNONE DETECTED
2nd rowUNKNOWN
3rd rowNONE DETECTED
4th rowNONE DETECTED
5th rowNONE DETECTED
ValueCountFrequency (%)
none 71593
22.0%
detected 71593
22.0%
of 29776
9.2%
suspect 29776
9.2%
use 29776
9.2%
not 29221
9.0%
alcohol 20406
 
6.3%
unknown 15367
 
4.7%
drug 15226
 
4.7%
n/a 4973
 
1.5%
Other values (8) 7006
 
2.2%
2024-12-04T13:02:10.838099image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 297863
15.2%
233382
11.9%
N 219346
11.2%
D 160329
 
8.2%
T 151384
 
7.7%
O 96431
 
4.9%
o 92407
 
4.7%
C 79145
 
4.0%
s 59552
 
3.0%
e 59552
 
3.0%
Other values (27) 507437
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1956828
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 297863
15.2%
233382
11.9%
N 219346
11.2%
D 160329
 
8.2%
T 151384
 
7.7%
O 96431
 
4.9%
o 92407
 
4.7%
C 79145
 
4.0%
s 59552
 
3.0%
e 59552
 
3.0%
Other values (27) 507437
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1956828
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 297863
15.2%
233382
11.9%
N 219346
11.2%
D 160329
 
8.2%
T 151384
 
7.7%
O 96431
 
4.9%
o 92407
 
4.7%
C 79145
 
4.0%
s 59552
 
3.0%
e 59552
 
3.0%
Other values (27) 507437
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1956828
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 297863
15.2%
233382
11.9%
N 219346
11.2%
D 160329
 
8.2%
T 151384
 
7.7%
O 96431
 
4.9%
o 92407
 
4.7%
C 79145
 
4.0%
s 59552
 
3.0%
e 59552
 
3.0%
Other values (27) 507437
25.9%

Non-Motorist Substance Abuse
Categorical

Imbalance  Missing 

Distinct25
Distinct (%)0.5%
Missing102147
Missing (%)95.5%
Memory size836.1 KiB
NONE DETECTED
3812 
Not Suspect of Alcohol Use, Not Suspect of Drug Use
513 
UNKNOWN
 
218
ALCOHOL PRESENT
 
147
ALCOHOL CONTRIBUTED
 
39
Other values (20)
 
127

Length

Max length157
Median length13
Mean length17.71561
Min length5

Characters and Unicode

Total characters86027
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowNONE DETECTED
2nd rowNONE DETECTED
3rd rowNONE DETECTED
4th rowNONE DETECTED
5th rowNONE DETECTED

Common Values

ValueCountFrequency (%)
NONE DETECTED 3812
 
3.6%
Not Suspect of Alcohol Use, Not Suspect of Drug Use 513
 
0.5%
UNKNOWN 218
 
0.2%
ALCOHOL PRESENT 147
 
0.1%
ALCOHOL CONTRIBUTED 39
 
< 0.1%
N/A, NONE DETECTED 25
 
< 0.1%
Not Suspect of Alcohol Use, Not Suspect of Drug Use, Not Suspect of Alcohol Use, Not Suspect of Drug Use 24
 
< 0.1%
Unknown, Unknown 18
 
< 0.1%
Suspect of Alcohol Use, Not Suspect of Drug Use 17
 
< 0.1%
Suspect of Alcohol Use, Unknown 8
 
< 0.1%
Other values (15) 35
 
< 0.1%
(Missing) 102147
95.5%

Length

2024-12-04T13:02:10.988109image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none 3841
26.7%
detected 3841
26.7%
suspect 1206
 
8.4%
of 1206
 
8.4%
use 1206
 
8.4%
not 1175
 
8.2%
alcohol 797
 
5.5%
drug 605
 
4.2%
unknown 271
 
1.9%
present 159
 
1.1%
Other values (8) 87
 
0.6%

Most occurring characters

ValueCountFrequency (%)
E 15740
18.3%
N 9766
11.4%
9538
11.1%
D 8336
9.7%
T 7937
9.2%
O 4496
 
5.2%
C 4082
 
4.7%
o 3645
 
4.2%
s 2412
 
2.8%
e 2412
 
2.8%
Other values (27) 17663
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86027
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 15740
18.3%
N 9766
11.4%
9538
11.1%
D 8336
9.7%
T 7937
9.2%
O 4496
 
5.2%
C 4082
 
4.7%
o 3645
 
4.2%
s 2412
 
2.8%
e 2412
 
2.8%
Other values (27) 17663
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86027
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 15740
18.3%
N 9766
11.4%
9538
11.1%
D 8336
9.7%
T 7937
9.2%
O 4496
 
5.2%
C 4082
 
4.7%
o 3645
 
4.2%
s 2412
 
2.8%
e 2412
 
2.8%
Other values (27) 17663
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86027
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 15740
18.3%
N 9766
11.4%
9538
11.1%
D 8336
9.7%
T 7937
9.2%
O 4496
 
5.2%
C 4082
 
4.7%
o 3645
 
4.2%
s 2412
 
2.8%
e 2412
 
2.8%
Other values (27) 17663
20.5%
Distinct64
Distinct (%)0.1%
Missing650
Missing (%)0.6%
Memory size836.1 KiB
2024-12-04T13:02:11.209095image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length54
Median length13
Mean length13.513441
Min length4

Characters and Unicode

Total characters1437195
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowOTHER VEHICLE
2nd rowPARKED VEHICLE
3rd rowPARKED VEHICLE
4th rowOTHER VEHICLE
5th rowPARKED VEHICLE
ValueCountFrequency (%)
vehicle 83589
38.0%
other 68381
31.1%
object 12917
 
5.9%
fixed 12042
 
5.5%
parked 10507
 
4.8%
motor 6504
 
3.0%
in 6454
 
2.9%
transport 6454
 
2.9%
pedestrian 4189
 
1.9%
off 1140
 
0.5%
Other values (78) 7526
 
3.4%
2024-12-04T13:02:11.559097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 263312
18.3%
H 144020
10.0%
113350
 
7.9%
I 100580
 
7.0%
C 91569
 
6.4%
T 91523
 
6.4%
O 84962
 
5.9%
V 83961
 
5.8%
R 83127
 
5.8%
L 78637
 
5.5%
Other values (46) 302154
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1437195
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 263312
18.3%
H 144020
10.0%
113350
 
7.9%
I 100580
 
7.0%
C 91569
 
6.4%
T 91523
 
6.4%
O 84962
 
5.9%
V 83961
 
5.8%
R 83127
 
5.8%
L 78637
 
5.5%
Other values (46) 302154
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1437195
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 263312
18.3%
H 144020
10.0%
113350
 
7.9%
I 100580
 
7.0%
C 91569
 
6.4%
T 91523
 
6.4%
O 84962
 
5.9%
V 83961
 
5.8%
R 83127
 
5.8%
L 78637
 
5.5%
Other values (46) 302154
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1437195
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 263312
18.3%
H 144020
10.0%
113350
 
7.9%
I 100580
 
7.0%
C 91569
 
6.4%
T 91523
 
6.4%
O 84962
 
5.9%
V 83961
 
5.8%
R 83127
 
5.8%
L 78637
 
5.5%
Other values (46) 302154
21.0%

Second Harmful Event
Text

Missing 

Distinct64
Distinct (%)0.2%
Missing79023
Missing (%)73.9%
Memory size836.1 KiB
2024-12-04T13:02:11.781098image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length76
Median length54
Mean length13.01258
Min length4

Characters and Unicode

Total characters364092
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowOTHER VEHICLE
2nd rowOTHER VEHICLE
3rd rowPARKED VEHICLE
4th rowFIXED OBJECT
5th rowEXPLOSION OR FIRE
ValueCountFrequency (%)
vehicle 15463
27.3%
other 13140
23.2%
object 7901
13.9%
fixed 7240
12.8%
parked 2536
 
4.5%
not 1687
 
3.0%
applicable 1687
 
3.0%
motor 807
 
1.4%
in 776
 
1.4%
transport 774
 
1.4%
Other values (82) 4638
 
8.2%
2024-12-04T13:02:12.160097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 60772
16.7%
28669
 
7.9%
H 27438
 
7.5%
O 23704
 
6.5%
I 23039
 
6.3%
T 22767
 
6.3%
C 22756
 
6.3%
R 17808
 
4.9%
V 16136
 
4.4%
L 14808
 
4.1%
Other values (46) 106195
29.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 364092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 60772
16.7%
28669
 
7.9%
H 27438
 
7.5%
O 23704
 
6.5%
I 23039
 
6.3%
T 22767
 
6.3%
C 22756
 
6.3%
R 17808
 
4.9%
V 16136
 
4.4%
L 14808
 
4.1%
Other values (46) 106195
29.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 364092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 60772
16.7%
28669
 
7.9%
H 27438
 
7.5%
O 23704
 
6.5%
I 23039
 
6.3%
T 22767
 
6.3%
C 22756
 
6.3%
R 17808
 
4.9%
V 16136
 
4.4%
L 14808
 
4.1%
Other values (46) 106195
29.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 364092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 60772
16.7%
28669
 
7.9%
H 27438
 
7.5%
O 23704
 
6.5%
I 23039
 
6.3%
T 22767
 
6.3%
C 22756
 
6.3%
R 17808
 
4.9%
V 16136
 
4.4%
L 14808
 
4.1%
Other values (46) 106195
29.2%

Junction
Categorical

Missing 

Distinct21
Distinct (%)< 0.1%
Missing27535
Missing (%)25.7%
Memory size836.1 KiB
INTERSECTION
32164 
NON INTERSECTION
23858 
INTERSECTION RELATED
10956 
Non-Junction
3745 
Intersection or Related
 
2707
Other values (16)
6038 

Length

Max length90
Median length30
Mean length15.090816
Min length5

Characters and Unicode

Total characters1199237
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNON INTERSECTION
2nd rowNON INTERSECTION
3rd rowNON INTERSECTION
4th rowNON INTERSECTION
5th rowOTHER

Common Values

ValueCountFrequency (%)
INTERSECTION 32164
30.1%
NON INTERSECTION 23858
22.3%
INTERSECTION RELATED 10956
 
10.2%
Non-Junction 3745
 
3.5%
Intersection or Related 2707
 
2.5%
COMMERCIAL DRIVEWAY 1162
 
1.1%
INTERCHANGE RELATED 1029
 
1.0%
OTHER 928
 
0.9%
Through Roadway 781
 
0.7%
RESIDENTIAL DRIVEWAY 486
 
0.5%
Other values (11) 1652
 
1.5%
(Missing) 27535
25.7%

Length

2024-12-04T13:02:12.294096image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
intersection 69685
55.3%
non 23858
 
18.9%
related 15540
 
12.3%
non-junction 3745
 
3.0%
or 3173
 
2.5%
driveway 2186
 
1.7%
other 1255
 
1.0%
commercial 1162
 
0.9%
interchange 1085
 
0.9%
through 781
 
0.6%
Other values (28) 3634
 
2.9%

Most occurring characters

ValueCountFrequency (%)
N 188271
15.7%
E 166832
13.9%
T 149824
12.5%
I 141835
11.8%
O 94119
7.8%
R 90267
7.5%
C 70889
 
5.9%
S 68271
 
5.7%
46636
 
3.9%
n 18003
 
1.5%
Other values (38) 164290
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1199237
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 188271
15.7%
E 166832
13.9%
T 149824
12.5%
I 141835
11.8%
O 94119
7.8%
R 90267
7.5%
C 70889
 
5.9%
S 68271
 
5.7%
46636
 
3.9%
n 18003
 
1.5%
Other values (38) 164290
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1199237
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 188271
15.7%
E 166832
13.9%
T 149824
12.5%
I 141835
11.8%
O 94119
7.8%
R 90267
7.5%
C 70889
 
5.9%
S 68271
 
5.7%
46636
 
3.9%
n 18003
 
1.5%
Other values (38) 164290
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1199237
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 188271
15.7%
E 166832
13.9%
T 149824
12.5%
I 141835
11.8%
O 94119
7.8%
R 90267
7.5%
C 70889
 
5.9%
S 68271
 
5.7%
46636
 
3.9%
n 18003
 
1.5%
Other values (38) 164290
13.7%

Intersection Type
Categorical

High correlation  Imbalance  Missing 

Distinct11
Distinct (%)< 0.1%
Missing55892
Missing (%)52.2%
Memory size836.1 KiB
FOUR-WAY INTERSECTION
30613 
T-INTERSECTION
13844 
OTHER
 
2581
Perpendicular
 
2171
Y-INTERSECTION
 
648
Other values (6)
 
1254

Length

Max length25
Median length21
Mean length17.667391
Min length5

Characters and Unicode

Total characters902998
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOTHER
2nd rowFOUR-WAY INTERSECTION
3rd rowUNKNOWN
4th rowT-INTERSECTION
5th rowT-INTERSECTION

Common Values

ValueCountFrequency (%)
FOUR-WAY INTERSECTION 30613
28.6%
T-INTERSECTION 13844
 
12.9%
OTHER 2581
 
2.4%
Perpendicular 2171
 
2.0%
Y-INTERSECTION 648
 
0.6%
Angled/Skewed 522
 
0.5%
ROUNDABOUT 302
 
0.3%
FIVE-POINT OR MORE 202
 
0.2%
TRAFFIC CIRCLE 117
 
0.1%
UNKNOWN 97
 
0.1%
(Missing) 55892
52.2%

Length

2024-12-04T13:02:12.432096image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
four-way 30613
37.2%
intersection 30613
37.2%
t-intersection 13844
16.8%
other 2581
 
3.1%
perpendicular 2171
 
2.6%
y-intersection 648
 
0.8%
angled/skewed 522
 
0.6%
roundabout 302
 
0.4%
five-point 202
 
0.2%
or 202
 
0.2%
Other values (5) 561
 
0.7%

Most occurring characters

ValueCountFrequency (%)
T 107270
11.9%
E 93312
10.3%
N 91005
10.1%
I 90848
10.1%
O 79606
8.8%
R 79253
8.8%
S 45627
 
5.1%
C 45470
 
5.0%
- 45307
 
5.0%
A 31554
 
3.5%
Other values (31) 193746
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 902998
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 107270
11.9%
E 93312
10.3%
N 91005
10.1%
I 90848
10.1%
O 79606
8.8%
R 79253
8.8%
S 45627
 
5.1%
C 45470
 
5.0%
- 45307
 
5.0%
A 31554
 
3.5%
Other values (31) 193746
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 902998
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 107270
11.9%
E 93312
10.3%
N 91005
10.1%
I 90848
10.1%
O 79606
8.8%
R 79253
8.8%
S 45627
 
5.1%
C 45470
 
5.0%
- 45307
 
5.0%
A 31554
 
3.5%
Other values (31) 193746
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 902998
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 107270
11.9%
E 93312
10.3%
N 91005
10.1%
I 90848
10.1%
O 79606
8.8%
R 79253
8.8%
S 45627
 
5.1%
C 45470
 
5.0%
- 45307
 
5.0%
A 31554
 
3.5%
Other values (31) 193746
21.5%

Road Alignment
Categorical

Imbalance  Missing 

Distinct12
Distinct (%)< 0.1%
Missing13896
Missing (%)13.0%
Memory size836.1 KiB
STRAIGHT
74048 
Straight
 
7354
CURVE RIGHT
 
5186
CURVE LEFT
 
4818
OTHER
 
533
Other values (7)
 
1168

Length

Max length33
Median length8
Mean length8.3029955
Min length5

Characters and Unicode

Total characters773067
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSTRAIGHT
2nd rowSTRAIGHT
3rd rowSTRAIGHT
4th rowCURVE LEFT
5th rowSTRAIGHT

Common Values

ValueCountFrequency (%)
STRAIGHT 74048
69.2%
Straight 7354
 
6.9%
CURVE RIGHT 5186
 
4.8%
CURVE LEFT 4818
 
4.5%
OTHER 533
 
0.5%
Curve Left 447
 
0.4%
Curve Right 392
 
0.4%
UNKNOWN 131
 
0.1%
Curve Left, Curve Right 75
 
0.1%
Curve Left, Straight 71
 
0.1%
Other values (2) 52
 
< 0.1%
(Missing) 13896
 
13.0%

Length

2024-12-04T13:02:12.569104image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
straight 81525
78.1%
curve 11118
 
10.6%
right 5705
 
5.5%
left 5413
 
5.2%
other 533
 
0.5%
unknown 131
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 158633
20.5%
R 90290
11.7%
S 81525
10.5%
H 79767
10.3%
I 79234
10.2%
G 79234
10.2%
A 74048
9.6%
t 16068
 
2.1%
E 15355
 
2.0%
11318
 
1.5%
Other values (19) 87595
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 773067
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 158633
20.5%
R 90290
11.7%
S 81525
10.5%
H 79767
10.3%
I 79234
10.2%
G 79234
10.2%
A 74048
9.6%
t 16068
 
2.1%
E 15355
 
2.0%
11318
 
1.5%
Other values (19) 87595
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 773067
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 158633
20.5%
R 90290
11.7%
S 81525
10.5%
H 79767
10.3%
I 79234
10.2%
G 79234
10.2%
A 74048
9.6%
t 16068
 
2.1%
E 15355
 
2.0%
11318
 
1.5%
Other values (19) 87595
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 773067
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 158633
20.5%
R 90290
11.7%
S 81525
10.5%
H 79767
10.3%
I 79234
10.2%
G 79234
10.2%
A 74048
9.6%
t 16068
 
2.1%
E 15355
 
2.0%
11318
 
1.5%
Other values (19) 87595
11.3%

Road Condition
Categorical

Imbalance  Missing 

Distinct21
Distinct (%)< 0.1%
Missing18803
Missing (%)17.6%
Memory size836.1 KiB
NO DEFECTS
80463 
No Defects
 
6258
OTHER
 
338
HOLES RUTS ETC
 
298
UNKNOWN
 
204
Other values (16)
 
639

Length

Max length24
Median length10
Mean length10.034195
Min length5

Characters and Unicode

Total characters885016
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNO DEFECTS
2nd rowNO DEFECTS
3rd rowNO DEFECTS
4th rowNO DEFECTS
5th rowNO DEFECTS

Common Values

ValueCountFrequency (%)
NO DEFECTS 80463
75.2%
No Defects 6258
 
5.8%
OTHER 338
 
0.3%
HOLES RUTS ETC 298
 
0.3%
UNKNOWN 204
 
0.2%
LOOSE SURFACE MATERIAL 157
 
0.1%
FOREIGN MATERIAL 127
 
0.1%
VIEW OBSTRUCTED 76
 
0.1%
SHOULDER DEFECT 71
 
0.1%
Not Applicable 52
 
< 0.1%
Other values (11) 156
 
0.1%
(Missing) 18803
 
17.6%

Length

2024-12-04T13:02:12.708093image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 86721
49.2%
defects 86721
49.2%
other 361
 
0.2%
holes 323
 
0.2%
ruts 323
 
0.2%
etc 323
 
0.2%
material 303
 
0.2%
unknown 248
 
0.1%
loose 170
 
0.1%
surface 170
 
0.1%
Other values (10) 653
 
0.4%

Most occurring characters

ValueCountFrequency (%)
E 163002
18.4%
88116
10.0%
N 87581
9.9%
D 86970
9.8%
O 82009
9.3%
T 82002
9.3%
S 81575
9.2%
C 81092
9.2%
F 80824
9.1%
e 12702
 
1.4%
Other values (30) 39143
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 885016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 163002
18.4%
88116
10.0%
N 87581
9.9%
D 86970
9.8%
O 82009
9.3%
T 82002
9.3%
S 81575
9.2%
C 81092
9.2%
F 80824
9.1%
e 12702
 
1.4%
Other values (30) 39143
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 885016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 163002
18.4%
88116
10.0%
N 87581
9.9%
D 86970
9.8%
O 82009
9.3%
T 82002
9.3%
S 81575
9.2%
C 81092
9.2%
F 80824
9.1%
e 12702
 
1.4%
Other values (30) 39143
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 885016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 163002
18.4%
88116
10.0%
N 87581
9.9%
D 86970
9.8%
O 82009
9.3%
T 82002
9.3%
S 81575
9.2%
C 81092
9.2%
F 80824
9.1%
e 12702
 
1.4%
Other values (30) 39143
 
4.4%

Road Division
Categorical

Imbalance  Missing 

Distinct32
Distinct (%)< 0.1%
Missing14676
Missing (%)13.7%
Memory size836.1 KiB
TWO-WAY, DIVIDED, POSITIVE MEDIAN BARRIER
39861 
TWO-WAY, NOT DIVIDED
30629 
TWO-WAY, DIVIDED, UNPROTECTED PAINTED MIN 4 FEET
9848 
Not Divided
 
3582
Divided, Raised Median (curbed)
 
3006
Other values (27)
5401 

Length

Max length125
Median length92
Mean length32.411256
Min length5

Characters and Unicode

Total characters2992434
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowTWO-WAY, NOT DIVIDED
2nd rowTWO-WAY, NOT DIVIDED
3rd rowTWO-WAY, NOT DIVIDED
4th rowTWO-WAY, DIVIDED, POSITIVE MEDIAN BARRIER
5th rowTWO-WAY, NOT DIVIDED

Common Values

ValueCountFrequency (%)
TWO-WAY, DIVIDED, POSITIVE MEDIAN BARRIER 39861
37.3%
TWO-WAY, NOT DIVIDED 30629
28.6%
TWO-WAY, DIVIDED, UNPROTECTED PAINTED MIN 4 FEET 9848
 
9.2%
Not Divided 3582
 
3.3%
Divided, Raised Median (curbed) 3006
 
2.8%
ONE-WAY TRAFFICWAY 1979
 
1.8%
OTHER 1166
 
1.1%
Divided, Flush Median (greater than 4ft wide) 730
 
0.7%
TWO-WAY, NOT DIVIDED WITH A CONTINUOUS LEFT TURN 367
 
0.3%
Divided, Depressed Median 297
 
0.3%
Other values (22) 862
 
0.8%
(Missing) 14676
 
13.7%

Length

2024-12-04T13:02:12.852096image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
divided 89677
22.5%
two-way 80705
20.2%
median 44639
11.2%
positive 39861
10.0%
barrier 39861
10.0%
not 35190
 
8.8%
unprotected 9848
 
2.5%
painted 9848
 
2.5%
min 9848
 
2.5%
4 9848
 
2.5%
Other values (21) 29897
 
7.5%

Most occurring characters

ValueCountFrequency (%)
I 343263
11.5%
D 311006
 
10.4%
306895
 
10.3%
E 253040
 
8.5%
T 195749
 
6.5%
A 176579
 
5.9%
W 166002
 
5.5%
O 165374
 
5.5%
R 136410
 
4.6%
, 136008
 
4.5%
Other values (37) 802108
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2992434
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 343263
11.5%
D 311006
 
10.4%
306895
 
10.3%
E 253040
 
8.5%
T 195749
 
6.5%
A 176579
 
5.9%
W 166002
 
5.5%
O 165374
 
5.5%
R 136410
 
4.6%
, 136008
 
4.5%
Other values (37) 802108
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2992434
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 343263
11.5%
D 311006
 
10.4%
306895
 
10.3%
E 253040
 
8.5%
T 195749
 
6.5%
A 176579
 
5.9%
W 166002
 
5.5%
O 165374
 
5.5%
R 136410
 
4.6%
, 136008
 
4.5%
Other values (37) 802108
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2992434
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 343263
11.5%
D 311006
 
10.4%
306895
 
10.3%
E 253040
 
8.5%
T 195749
 
6.5%
A 176579
 
5.9%
W 166002
 
5.5%
O 165374
 
5.5%
R 136410
 
4.6%
, 136008
 
4.5%
Other values (37) 802108
26.8%

Latitude
Real number (ℝ)

High correlation 

Distinct94371
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.084405
Minimum37.72
Maximum39.990414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size836.1 KiB
2024-12-04T13:02:12.991096image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum37.72
5-th percentile38.985993
Q139.025862
median39.076031
Q339.14087
95-th percentile39.202927
Maximum39.990414
Range2.270414
Interquartile range (IQR)0.1150079

Descriptive statistics

Standard deviation0.072786604
Coefficient of variation (CV)0.0018622927
Kurtosis3.5488018
Mean39.084405
Median Absolute Deviation (MAD)0.05514808
Skewness0.56472941
Sum4182148.6
Variance0.0052978897
MonotonicityNot monotonic
2024-12-04T13:02:13.133110image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.953 26
 
< 0.1%
39.11342767 15
 
< 0.1%
39.04627667 13
 
< 0.1%
39.045425 13
 
< 0.1%
39.11061 10
 
< 0.1%
39.07997592 10
 
< 0.1%
39.109775 10
 
< 0.1%
39.05350158 9
 
< 0.1%
39.07676629 9
 
< 0.1%
39.08277 8
 
< 0.1%
Other values (94361) 106880
99.9%
ValueCountFrequency (%)
37.72 1
 
< 0.1%
38.00812 1
 
< 0.1%
38.353495 1
 
< 0.1%
38.55400492 1
 
< 0.1%
38.66493 1
 
< 0.1%
38.67069811 1
 
< 0.1%
38.743373 6
< 0.1%
38.77121637 1
 
< 0.1%
38.78370267 1
 
< 0.1%
38.81807478 1
 
< 0.1%
ValueCountFrequency (%)
39.990414 1
 
< 0.1%
39.98974718 1
 
< 0.1%
39.988369 1
 
< 0.1%
39.987474 1
 
< 0.1%
39.972695 1
 
< 0.1%
39.96483333 1
 
< 0.1%
39.8729 1
 
< 0.1%
39.83806818 1
 
< 0.1%
39.737 1
 
< 0.1%
39.72 7
< 0.1%

Longitude
Real number (ℝ)

High correlation 

Distinct96536
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-77.11429
Minimum-79.486
Maximum-75.527708
Zeros0
Zeros (%)0.0%
Negative107003
Negative (%)100.0%
Memory size836.1 KiB
2024-12-04T13:02:13.271109image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum-79.486
5-th percentile-77.267614
Q1-77.191849
median-77.107518
Q3-77.04049
95-th percentile-76.973054
Maximum-75.527708
Range3.9582921
Interquartile range (IQR)0.15135877

Descriptive statistics

Standard deviation0.099364349
Coefficient of variation (CV)-0.0012885335
Kurtosis29.984613
Mean-77.11429
Median Absolute Deviation (MAD)0.07685333
Skewness-1.3938816
Sum-8251460.4
Variance0.0098732739
MonotonicityNot monotonic
2024-12-04T13:02:13.424096image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-77.338 26
 
< 0.1%
-76.990695 15
 
< 0.1%
-77.23648183 15
 
< 0.1%
-76.99073667 13
 
< 0.1%
-76.98979833 10
 
< 0.1%
-76.91044 10
 
< 0.1%
-77.13826298 10
 
< 0.1%
-77.04575667 9
 
< 0.1%
-77.11714305 9
 
< 0.1%
-77.08949028 9
 
< 0.1%
Other values (96526) 106877
99.9%
ValueCountFrequency (%)
-79.486 7
< 0.1%
-79.48 1
 
< 0.1%
-79.42565918 1
 
< 0.1%
-79.18192616 1
 
< 0.1%
-78.71704102 1
 
< 0.1%
-78.21582167 1
 
< 0.1%
-77.91778564 1
 
< 0.1%
-77.75 1
 
< 0.1%
-77.65175333 1
 
< 0.1%
-77.54699707 6
< 0.1%
ValueCountFrequency (%)
-75.52770787 1
< 0.1%
-75.97595215 1
< 0.1%
-75.98250506 1
< 0.1%
-76.18469238 1
< 0.1%
-76.26731339 1
< 0.1%
-76.32256482 1
< 0.1%
-76.4702388 1
< 0.1%
-76.47317333 1
< 0.1%
-76.519245 1
< 0.1%
-76.56337646 1
< 0.1%
Distinct106136
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size836.1 KiB
2024-12-04T13:02:13.817100image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length27
Median length27
Mean length25.801931
Min length13

Characters and Unicode

Total characters2760884
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique105576 ?
Unique (%)98.7%

Sample

1st row(39.11311333, -77.05759167)
2nd row(38.98244333, -77.079235)
3rd row(39.189845, -77.230325)
4th row(39.04169833, -77.050125)
5th row(39.08472, -77.1482)
ValueCountFrequency (%)
38.953 26
 
< 0.1%
77.338 26
 
< 0.1%
39.11342767 15
 
< 0.1%
77.23648183 15
 
< 0.1%
76.990695 15
 
< 0.1%
39.04627667 13
 
< 0.1%
39.045425 13
 
< 0.1%
76.99073667 13
 
< 0.1%
76.91044 10
 
< 0.1%
39.109775 10
 
< 0.1%
Other values (190897) 213850
99.9%
2024-12-04T13:02:14.475099image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 358069
13.0%
3 320763
11.6%
9 233190
 
8.4%
. 214006
 
7.8%
1 198644
 
7.2%
0 187338
 
6.8%
6 182718
 
6.6%
8 148237
 
5.4%
5 136538
 
4.9%
2 135627
 
4.9%
Other values (6) 645754
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2760884
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 358069
13.0%
3 320763
11.6%
9 233190
 
8.4%
. 214006
 
7.8%
1 198644
 
7.2%
0 187338
 
6.8%
6 182718
 
6.6%
8 148237
 
5.4%
5 136538
 
4.9%
2 135627
 
4.9%
Other values (6) 645754
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2760884
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 358069
13.0%
3 320763
11.6%
9 233190
 
8.4%
. 214006
 
7.8%
1 198644
 
7.2%
0 187338
 
6.8%
6 182718
 
6.6%
8 148237
 
5.4%
5 136538
 
4.9%
2 135627
 
4.9%
Other values (6) 645754
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2760884
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 358069
13.0%
3 320763
11.6%
9 233190
 
8.4%
. 214006
 
7.8%
1 198644
 
7.2%
0 187338
 
6.8%
6 182718
 
6.6%
8 148237
 
5.4%
5 136538
 
4.9%
2 135627
 
4.9%
Other values (6) 645754
23.4%

Interactions

2024-12-04T13:01:58.130713image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T13:01:57.448715image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T13:01:57.800712image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T13:01:58.251717image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T13:01:57.568713image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T13:01:57.909713image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T13:01:58.362714image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T13:01:57.674727image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-04T13:01:58.012714image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2024-12-04T13:02:14.603096image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ACRS Report TypeAgency NameAt FaultCollision TypeDirectionDistanceDistance UnitHit/RunIntersection TypeJunctionLane DirectionLatitudeLightLongitudeMunicipalityNon-Motorist Substance AbuseRelated Non-MotoristRoad AlignmentRoad ConditionRoad DivisionRoad GradeRoute TypeSurface ConditionWeather
ACRS Report Type1.0000.0360.1250.1620.0230.0000.0140.2050.0420.0820.0370.0230.0550.0190.0490.2290.2050.0320.0190.0520.0300.0490.0290.037
Agency Name0.0361.0000.1800.2510.0550.0140.0820.1110.2220.2570.2500.0920.2490.1340.4260.2770.2690.2480.2360.2530.2500.3750.2490.252
At Fault0.1250.1801.0000.2590.0170.0160.0110.0630.1870.2360.2210.0170.2360.0000.0490.2580.1860.2240.2100.2250.2230.2180.2240.239
Collision Type0.1620.2510.2591.0000.0860.0440.1480.3160.3330.2780.2420.0480.2890.0410.0620.2110.2790.3340.2290.2150.2330.2380.2370.231
Direction0.0230.0550.0170.0861.0000.0000.0470.0140.0770.1470.3460.0210.0480.0240.1420.0520.0480.0560.0420.0810.0500.1660.0450.044
Distance0.0000.0140.0160.0440.0001.0000.0000.0001.0000.0580.034-0.0030.0230.0131.0000.2490.0680.0220.0170.0280.0210.0240.0180.017
Distance Unit0.0140.0820.0110.1480.0470.0001.0000.0380.1390.2200.0940.0630.1110.0420.0820.0000.0400.1360.0900.0970.0930.1230.0850.075
Hit/Run0.2050.1110.0630.3160.0140.0000.0381.0000.0840.1330.1030.0540.1760.0600.1150.0580.0240.1070.1050.1120.1130.1010.1530.165
Intersection Type0.0420.2220.1870.3330.0771.0000.1390.0841.0000.4160.3300.0310.3310.0210.1020.3270.3210.3860.3520.3890.3590.3220.3340.324
Junction0.0820.2570.2360.2780.1470.0580.2200.1330.4161.0000.2470.0220.2670.0280.0510.2340.2410.3450.2380.2720.2470.2720.2370.227
Lane Direction0.0370.2500.2210.2420.3460.0340.0940.1030.3300.2471.0000.0000.2640.0000.1080.2920.2840.3240.2280.2280.2570.2400.2300.220
Latitude0.0230.0920.0170.0480.021-0.0030.0630.0540.0310.0220.0001.0000.022-0.6360.3480.0000.0000.0280.0020.0300.0160.0650.0080.009
Light0.0550.2490.2360.2890.0480.0230.1110.1760.3310.2670.2640.0221.0000.0160.0410.2760.2720.3100.2760.2690.2650.2500.2850.322
Longitude0.0190.1340.0000.0410.0240.0130.0420.0600.0210.0280.000-0.6360.0161.0000.4900.0000.0000.0280.0070.0100.0120.1270.0700.019
Municipality0.0490.4260.0490.0620.1421.0000.0820.1150.1020.0510.1080.3480.0410.4901.0000.0000.0400.0880.0260.1060.0610.1420.0530.016
Non-Motorist Substance Abuse0.2290.2770.2580.2110.0520.2490.0000.0580.3270.2340.2920.0000.2760.0000.0001.0000.2690.3800.2910.2790.3010.2520.3180.250
Related Non-Motorist0.2050.2690.1860.2790.0480.0680.0400.0240.3210.2410.2840.0000.2720.0000.0400.2691.0000.3870.3010.3390.3090.2630.3040.272
Road Alignment0.0320.2480.2240.3340.0560.0220.1360.1070.3860.3450.3240.0280.3100.0280.0880.3800.3871.0000.3350.3610.3960.3010.3150.309
Road Condition0.0190.2360.2100.2290.0420.0170.0900.1050.3520.2380.2280.0020.2760.0070.0260.2910.3010.3351.0000.2570.2580.2280.2840.249
Road Division0.0520.2530.2250.2150.0810.0280.0970.1120.3890.2720.2280.0300.2690.0100.1060.2790.3390.3610.2571.0000.2800.2570.2390.225
Road Grade0.0300.2500.2230.2330.0500.0210.0930.1130.3590.2470.2570.0160.2650.0120.0610.3010.3090.3960.2580.2801.0000.2280.2400.224
Route Type0.0490.3750.2180.2380.1660.0240.1230.1010.3220.2720.2400.0650.2500.1270.1420.2520.2630.3010.2280.2570.2281.0000.2280.228
Surface Condition0.0290.2490.2240.2370.0450.0180.0850.1530.3340.2370.2300.0080.2850.0700.0530.3180.3040.3150.2840.2390.2400.2281.0000.466
Weather0.0370.2520.2390.2310.0440.0170.0750.1650.3240.2270.2200.0090.3220.0190.0160.2500.2720.3090.2490.2250.2240.2280.4661.000

Missing values

2024-12-04T13:01:58.691714image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-04T13:01:59.485714image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-04T13:02:00.916714image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Report NumberLocal Case NumberAgency NameACRS Report TypeCrash Date/TimeHit/RunRoute TypeLane DirectionLane TypeNumber of LanesDirectionDistanceDistance UnitRoad GradeRoad NameCross-Street NameOff-Road DescriptionMunicipalityRelated Non-MotoristAt FaultCollision TypeWeatherSurface ConditionLightTraffic ControlDriver Substance AbuseNon-Motorist Substance AbuseFirst Harmful EventSecond Harmful EventJunctionIntersection TypeRoad AlignmentRoad ConditionRoad DivisionLatitudeLongitudeLocation
0MCP1123002M190010046Montgomery County PoliceInjury Crash03/04/2019 08:41:00 AMNoMaryland (State)WestNaN2East200.0FEETGRADE DOWNHILLNORBECK RDWINTERGATE DRNaNNaNNaNDRIVERSAME DIR REAR ENDCLOUDYDRYDAYLIGHTNaNNONE DETECTEDNaNOTHER VEHICLENaNNON INTERSECTIONNaNSTRAIGHTNO DEFECTSTWO-WAY, NOT DIVIDED39.113113-77.057592(39.11311333, -77.05759167)
1MCP2161000916028039Montgomery County PoliceProperty Damage Crash06/04/2016 07:14:00 PMYesCountyEastNaN1East500.0FEETLEVELTHORNAPPLE STLENHART DRNaNNaNNaNDRIVEROTHERCLEARDRYDAYLIGHTNaNUNKNOWNNaNPARKED VEHICLEOTHER VEHICLENON INTERSECTIONNaNSTRAIGHTNO DEFECTSTWO-WAY, NOT DIVIDED38.982443-77.079235(38.98244333, -77.079235)
2MCP2790000P15041420MONTGOMERYProperty Damage Crash08/18/2015 11:00:00 PMNoCountySouthNaN2South30.0FEETLEVELVALLEY BEND DRCROSS LAUREL DRNaNNaNNaNUNKNOWNOPPOSITE DIRECTION SIDESWIPECLEARDRYDARK LIGHTS ONNO CONTROLSNONE DETECTEDNaNPARKED VEHICLENaNNON INTERSECTIONNaNSTRAIGHTNO DEFECTSTWO-WAY, NOT DIVIDED39.189845-77.230325(39.189845, -77.230325)
3MCP3378000J230051006Montgomery County PoliceInjury Crash08/24/2023 07:46:00 AMNoMaryland (State)WestNaN4West50.0FEETLEVELUNIVERSITY BLVD WELKIN STNaNNaNNaNDRIVERSINGLE VEHICLECLOUDYDRYDAYLIGHTNO CONTROLSNONE DETECTEDNaNNaNNaNNON INTERSECTIONNaNCURVE LEFTNO DEFECTSTWO-WAY, DIVIDED, POSITIVE MEDIAN BARRIER39.041698-77.050125(39.04169833, -77.050125)
4DD5659000H230049130Rockville Police DepartmeProperty Damage Crash08/12/2023 04:28:00 PMYesNaNSouthNaN3South40.0FEETLEVELROCKVILLE PIKEPARK RDNaNNaNNaNDRIVERSAME DIRECTION SIDESWIPENaNDRYNaNNO CONTROLSNaNNaNOTHER VEHICLEOTHER VEHICLENaNNaNSTRAIGHTNO DEFECTSTWO-WAY, NOT DIVIDED39.084720-77.148200(39.08472, -77.1482)
5MCP33190021230049349Montgomery County PoliceProperty Damage Crash08/14/2023 08:50:00 AMNoCountyUnknownOTHER0East400.0FEETNaNFENTON STBONIFANT STNaNNaNNaNDRIVERSINGLE VEHICLENaNNaNDAYLIGHTNaNNaNNaNNaNNaNOTHERNaNNaNNaNNaN38.994612-77.023368(38.99461167, -77.02336833)
6MCP3008003Z230059393Montgomery County PoliceProperty Damage Crash10/09/2023 03:40:00 AMNoCountyEastNaN2East50.0FEETLEVELDILSTON RDMOFFET RDNaNNaNNaNDRIVERHEAD ONCLEARDRYDARK LIGHTS ONNO CONTROLSNaNNaNPARKED VEHICLEPARKED VEHICLENON INTERSECTIONNaNSTRAIGHTNO DEFECTSTWO-WAY, NOT DIVIDED39.018215-76.983405(39.018215, -76.983405)
7MCP289200FC230059753Montgomery County PoliceProperty Damage Crash10/10/2023 08:59:00 PMNoCountySouthNaN1North50.0FEETLEVELBRUNETT AVELYCOMING STNaNNaNNaNDRIVERHEAD ONCLEARDRYDARK LIGHTS ONNO CONTROLSNONE DETECTEDNaNPARKED VEHICLENaNNaNNaNSTRAIGHTNaNTWO-WAY, NOT DIVIDED39.011715-77.021583(39.011715, -77.02158333)
8MCP2821002X230034109Montgomery County PoliceProperty Damage Crash07/17/2023 11:37:00 PMYesCountySouthNaN2South0.5MILELEVELFOUNDERS MILL DRFOUNDERS MILL CTNaNNaNNaNDRIVERSINGLE VEHICLECLEARDRYDARK -- UNKNOWN LIGHTINGNO CONTROLSALCOHOL PRESENTNaNFIXED OBJECTFIXED OBJECTNaNNaNCURVE LEFTNO DEFECTSTWO-WAY, NOT DIVIDED39.139848-77.150410(39.1398478, -77.15041002)
9MCP2771002W230061610Montgomery County PoliceProperty Damage Crash10/20/2023 12:41:00 PMYesCountyEastNaN2East10.0FEETNaNFREDALE STFARTHING DRNaNNaNNaNDRIVEROTHERCLOUDYDRYDAYLIGHTNaNNaNNaNPARKED VEHICLENaNNaNNaNSTRAIGHTNO DEFECTSTWO-WAY, NOT DIVIDED39.061433-77.069168(39.06143272, -77.06916793)
Report NumberLocal Case NumberAgency NameACRS Report TypeCrash Date/TimeHit/RunRoute TypeLane DirectionLane TypeNumber of LanesDirectionDistanceDistance UnitRoad GradeRoad NameCross-Street NameOff-Road DescriptionMunicipalityRelated Non-MotoristAt FaultCollision TypeWeatherSurface ConditionLightTraffic ControlDriver Substance AbuseNon-Motorist Substance AbuseFirst Harmful EventSecond Harmful EventJunctionIntersection TypeRoad AlignmentRoad ConditionRoad DivisionLatitudeLongitudeLocation
106993MCP3405000C240055835MONTGOMERYInjury Crash11/21/2024 08:16:00 AMNoMaryland (State) RouteEastbound, WestboundLane 2, Other2East0.00FEETLevelCLOPPER RDSCHAEFFER RDNaNNaNNaNDRIVERSideswipe, Opposite DirectionClearDryDaylightNo ControlsNot Suspect of Alcohol Use, Not Suspect of Drug Use, Not Suspect of Alcohol Use, Not Suspect of Drug UseNaNMotor Vehicle In TransportNot ApplicableIntersection or RelatedPerpendicularStraightNo DefectsDivided, Raised Median (curbed)39.163507-77.284726(39.16350716, -77.28472559)
106994MCP3379001F240056447MONTGOMERYProperty Damage Crash11/24/2024 07:30:00 PMNoNaNNaNNaN0NaNNaNFEETNaNNaNNaNParking Lot Way NORTWEST HIGH SCHOOL PARKING LOTNaNNaNDRIVERSingle VehicleClearNaNDark - LightedNaNNot Suspect of Alcohol Use, Not Suspect of Drug UseNaNOther Non-Fixed ObjectNaNNaNNaNNaNNaNNaN39.151934-77.279724(39.15193366, -77.27972385)
106995MCP32620053240055176MONTGOMERYProperty Damage Crash11/17/2024 05:45:00 PMNaNCounty RouteEastbound, WestboundLane 14West34.77FEETLevelOLD COLUMBIA PIKEFAIRLAND RD (WB/L)NaNNaNNaNDRIVERSideswipe, Opposite DirectionClearDryDark - LightedTraffic Control SignalNot Suspect of Alcohol Use, Not Suspect of Drug Use, Not Suspect of Alcohol Use, Not Suspect of Drug UseNaNMotor Vehicle In TransportNaNNaNNaNStraightNo DefectsNot Divided39.076057-76.957597(39.0760575, -76.95759667)
106996MCP2693004K240056200MONTGOMERYProperty Damage Crash11/23/2024 08:10:00 AMNoMaryland (State) RouteEastboundLane 11South0.00FEETUphillMUNCASTER MILL RDEMORY LANaNNaNNaNDRIVERSingle VehicleClearDryDaylightTraffic Control SignalNot Suspect of Alcohol Use, Not Suspect of Drug UseNaNTraffic Sign SupportCurbIntersection or RelatedPerpendicularStraightNo DefectsDivided, Raised Median (curbed)39.116247-77.099056(39.11624669, -77.09905598)
106997MCP3405000B240055099MONTGOMERYProperty Damage Crash11/17/2024 07:04:00 AMNaNNaNNaNNaN0NaNNaNFEETNaNNaNNaNParking Lot Way PARKING LOT OF 12820 LOCBURY CIRCLE APT B, GERMANTOWN, MD 20874NaNNaNDRIVERRear To SideClearNaNUnknownNaNUnknown, UnknownNaNParked VehicleNaNNaNNaNNaNNaNNaN39.185115-77.264976(39.1851153, -77.2649756)
106998MCP3390001D240055757MONTGOMERYProperty Damage Crash11/20/2024 05:14:00 PMNaNNaNNaNNaN0NaNNaNFEETNaNNaNNaNParking Aisle PARKING LOTNaNNaNUNKNOWNSideswipe, Opposite DirectionClearNaNDark - LightedNaNUnknown, UnknownNaNParked VehicleNaNNaNNaNNaNNaNNaN39.175948-77.279133(39.17594754, -77.27913298)
106999MCP32620054240055185MONTGOMERYProperty Damage Crash11/17/2024 06:45:00 PMNoRampSouthboundLane 11North17.53FEETLevelRAMP 5 FR RAMP 6 (US29) TO BLACKBURN RDNaNNaNNaNNaNDRIVERSingle VehicleClearDryDark - Not LightedNo ControlsNot Suspect of Alcohol Use, Not Suspect of Drug UseNaNGuardrail FaceNaNNaNNaNStraightNo DefectsDivided, Depressed Median39.107257-76.931971(39.10725667, -76.931971)
107000DD56530042240056865ROCKVILLEInjury Crash11/26/2024 08:50:00 PMNoMunicipality RouteEastbound, WestboundLane 1, Left Turn Lane4, 5North0.00FEETLevelTAFT CTTAFT STNaNNaNNaNUNKNOWNFront to FrontClearDryDark - LightedTraffic Control SignalNot Suspect of Alcohol Use, Not Suspect of Drug Use, Not Suspect of Alcohol Use, Not Suspect of Drug UseNaNMotor Vehicle In TransportCurbIntersection or RelatedPerpendicularStraightNo DefectsDivided, Raised Median (curbed)39.094150-77.132558(39.09414975, -77.13255762)
107001MCP3095005G240055058MONTGOMERYProperty Damage Crash11/16/2024 07:38:00 PMNoCounty RouteEastbound, WestboundLane 1, Left Turn Lane4South47.75FEETLevelRANDOLPH RD (WB/L)NaNNaNNaNNaNUNKNOWNOtherClearDryDark - LightedOtherNot Suspect of Alcohol Use, Not Suspect of Drug Use, Not Suspect of Alcohol Use, Not Suspect of Drug UseNaNMotor Vehicle In TransportMotor Vehicle In TransportNon-JunctionNaNStraightNo DefectsDivided, Flush Median (greater than 4ft wide), Divided, Raised Median (curbed)39.066655-77.019891(39.06665483, -77.01989117)
107002MCP2466005J240056283MONTGOMERYInjury Crash11/23/2024 05:15:00 PMNoMaryland (State) RouteEastboundLane 12South45.66FEETLevelDARNESTOWN RDNaNNaNNaNNaNUNKNOWNFront to RearClearDryDark - LightedNo ControlsNot Suspect of Alcohol Use, Not Suspect of Drug Use, Unknown, UnknownNaNMotor Vehicle In TransportNaNNon-JunctionNaNStraightNo DefectsNot Divided39.104552-77.293886(39.10455216, -77.29388633)